a novel vascular intervention surgical robot based on

applied sciences

Article

A Novel Vascular Intervention Surgical Robot Based on ForceFeedback and Flexible Clamping

Haoyang Yu 1,2, Hongbo Wang 1,3,*, Jingyuan Chang 2, Jianye Niu 2, Fuhao Wang 3, Yonggan Yan 2, Hesuo Tian 2,Junyu Fang 2 and Haixia Lu 2

��

Citation: Yu, H.; Wang, H.; Chang, J.;

Niu, J.; Wang, F.; Yan, Y.; Tian, H.;

Fang, J.; Lu, H. A Novel Vascular

Intervention Surgical Robot Based on

Force Feedback and Flexible

Clamping. Appl. Sci. 2021, 11, 611.

https://doi.org/10.3390/app11020611

Received: 10 December 2020

Accepted: 7 January 2021

Published: 10 January 2021

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1 Parallel Robot and Mechatronic System Laboratory of Hebei Province, Yanshan University,Qinhuangdao 066004, China; [email protected]

2 Key Laboratory of Advanced Forging & Stamping Technology and Science of Ministry of Education,Yanshan University, Qinhuangdao 066004, China; [email protected] (J.C.); [email protected] (J.N.);[email protected] (Y.Y.); [email protected] (H.T.); [email protected] (J.F.);[email protected] (H.L.)

3 Academy for Engineering & Technology, Fudan University, Shanghai 200433, China;[email protected]

* Correspondence: [email protected]; Tel.: +86-1393-366-5525

Abstract: At present, most vascular intervention surgical robots (VISRs) cannot achieve effectiveforce feedback and lack regulation of the clamping force of the guidewire. In this paper, a VISRbased on force feedback and clamping force regulation is proposed. It is a master–slave systemconsisting of a master manipulator that is flexible enough and a slave wire feeder that can deliverthe guidewire. Accurate force feedback is established to ensure the safety of the operation, andthe clamping force of the guidewire can be regulated in real time. Based on the dynamic analysisof the mechanism, the control scheme of the system is designed. The two-dimensional fuzzy PID(Proportion Integration Differentiation) controller is equipped with on-line tuning parameters andanti-interference capabilities. The sine and step signals are selected to carry out simulation analysison the controller. The performance of the designed VISR was verified by a force feedback experiment,a clamping force regulation experiment and a vascular model experiment.

Keywords: vascular interventional surgical robot; force feedback; flexible clamping; fuzzy PID controller

1. Introduction

Vascular interventional surgery means that under the guidance of medical imaging,the doctor manipulates the catheter or guidewire into the human blood vessel througha small wound and then moves to the lesion for treatment, so as to embolize the mal-formed blood vessel, dissolve the thrombus, dilate the narrow blood vessel and so on [1–3].However, doctors who have been exposed to radiation for a long time cannot completelyeliminate radiation hazards, even if they wear radiation-proof lead clothing [4,5], and heavyprotective clothing will also bring doctors cervical or lumbar pain [6]. In addition, due tothe bending and narrowing of human blood vessels and their many branches, the doctor isrequired to not shake too much during the operation. As such, the doctor should be highlyfocused during the operation, which can easily cause fatigue in the doctor, thus leaving himor her unable to guarantee the success and accuracy of the operation [7]. The application ofrobot technology to the field of vascular interventional technology can largely relieve theburden of doctors and free them from the high radiation environment [8]. With the help ofrobot technology, surgical safety and accuracy can be improved, and the accidents causedby human fatigue factors can be reduced [9,10].

In recent years, several commercial vascular intervention surgical robot (VISR) sys-tems have been developed. For example, the Sensei robot system developed by HansenMedical has been successfully used in clinical applications in different fields for remote

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Appl. Sci. 2021, 11, 611 2 of 19

operations using operable catheters and jackets [11,12]. The Amigo robot system, designedby Catheter Robotics, has three degrees of freedom and can carry out such actions as thepropulsion of the active catheter, twisting the catheter and bending the catheter head [13,14].The Niobe robot system developed by Stereotaxis Incorporation manages the position andincorporation of the catheter by using magnetic navigation technology [15,16]. All three areactive catheter systems, but issues such as the size and safety of active catheters limit theiruse in cardiovascular surgery, compared with the wide range of passive catheters. Cur-rently, the most mature passive catheter system is the Corpath GRX robot system [17–19]developed by Corindus Vascular Robotics, which includes a wire feeder and a remoteoperating station for the operation of catheters, guidewires, balloons and other instruments.In addition, some research institutions have also conducted research in this field, such asHanyang University, the University of Western Ontario, the Beijing Institute of Technologyand Shanghai University [20–23].

In remote interventional surgery, in addition to the guidance of medical images,doctors also need to use the assistance of robots to judge whether the front end of theguidewire touches the blood vessel [24]. Therefore, the establishment of force feedbackhas become an important factor affecting the success rate of a surgery, which includestwo aspects: the accurate detection of proximal force at the slave end and the formationof force sensation at the master end. However, most of the VISR system only tests theproximal force without providing the operator with a force sensation [25,26]. Otherwise,some agencies use commercial master manipulators, such as the Geomagic Touch X systemdeveloped by 3D Systems, which is not flexible enough for vascular intervention surgeryand is not in accordance with traditional surgical operations [27].

During an operation, doctors use their fingers to propel and twist the catheter orguidewire, which can sense the degree of clamping and adjust the clamping force in realtime. When the VISR is used for surgery, there are two methods for clamping the catheteror guidewire: rolling transmission with a friction wheel [28] and reciprocating propulsionwith a sliding platform [29]. Neither of these two methods are able to perform flexibleclamping on the catheter or guidewire; that is, the doctor cannot sense the clamping forcein real time and adjust it. Moreover, the former is difficult to reliably clamp due to itsslippage between the friction wheel and the catheter or guidewire, while the latter willdamage the surface of the catheter or guidewire if the clamping force is too large [30].Therefore, flexible clamping of the catheter or guidewire is critical, but few researchershave conducted research based on flexible clamping.

In light of these problems, a novel vascular intervention surgical robot (VISR) basedon force feedback and flexible clamping was designed. In Section 2, the system is describedin detail, and the mechanisms of force feedback and flexible clamping are introduced. InSection 3, the dynamics of the system are analyzed. In Section 4, the two-dimensional fuzzyPID controller is designed according to the dynamic model, and its function is analyzed.Four types of experiments to evaluate the performance of the proposed VISR are introducedin Section 5, and the results obtained are discussed. Finally, our conclusions are given inSection 6.

2. System Description

The designed VISR is a master–slave system, including the master manipulator, theslave wire feeder, the control system and the force feedback system between them. Theworkflow and information interaction of different components in the VISR are shown inFigure 1. The doctor operates the master manipulator in a safe isolation room. The controlsystem then receives this operating signal and issues control instructions to the slave wirefeeder. Then, the slave wire feeder copies the doctor’s operation in the operating room toflexibly clamp and deliver the catheter or guidewire, so as to retain the doctor’s dexterity onthe premise of ensuring the doctor’s safety. In this process, the slave wire feeder measuresthe proximal force of the guidewire in real time, and the master manipulator forms a forcesensation according to this, thus ensuring the safety of the surgery.

Appl. Sci. 2021, 11, 611 3 of 19

Appl. Sci. 2021, 11, x FOR PEER REVIEW 3 of 19

on the premise of ensuring the doctor’s safety. In this process, the slave wire feeder

measures the proximal force of the guidewire in real time, and the master manipulator

forms a force sensation according to this, thus ensuring the safety of the surgery.

Master manipulator

Control system

Slave wire feeder

DSA

Computer

PatientDoctor

Forcefeedback

Operating

Master side Slave side

Medicalimage

Delivery guidewire

Forcesignal

Force feedback system

Figure 1. Schematic diagram of the system.

2.1. Master Manipulator

The master manipulator includes an operating component, a position detection com-

ponent, a force sensation component and an image component, as shown in Figure 2. The

operating component is composed of a spline shaft, operating handle, fixed shaft and

bearing block. The damped operating handle slides on the spline shaft. The fixed shaft is

attached to both sides of the spline shaft to limit its axial movement. The position detection

component consists of angle sensor-A, angle sensor-B (P2500, Miran Technology, Shen-

zhen, CN), a synchronous belt and a pulley. The operating handle is fixed with the syn-

chronous belt, and the propulsion information is converted into rotation angle infor-

mation through the synchronous belt group and then collected through angle sensor-A,

connected to the left end of the synchronous belt group. Angle sensor-B is connected with

the extended end of the fixed shaft through a coupling to collect the rotation information.

The resolution of the angle sensor is 0.355°, the independent linearity is ±0.1%, and the

repeatability is 0.01°. The force sensation component is composed of a servo motor (RE10,

Maxon Motor, Canton of Upper Walden, Switzerland) and a magnetic powder clutch (CD-

HSY-5, Lanling, Haian, CN). The right end of the synchronous belt group is connected

with the output end of the magnetic powder clutch, and the input end of the magnetic

powder clutch is connected with the servo motor through the coupling. Image compo-

nents are used to display intraoperative force–position information and assist doctors to

judge the state of the guidewire tip.

Servo motor

Magnetic powder clutch Pulley

Operating handle

Spline shaft

Angle sensor-A

Synchronous belt

Bearing block

Fixed shaft

Angle sensor-B

Image component



The master manipulator includes an operating component, a position detection com-ponent, a force sensation component and an image component, as shown in Figure 2. Theoperating component is composed of a spline shaft, operating handle, fixed shaft andbearing block. The damped operating handle slides on the spline shaft. The fixed shaft isattached to both sides of the spline shaft to limit its axial movement. The position detectioncomponent consists of angle sensor-A, angle sensor-B (P2500, Miran Technology, Shenzhen,CN), a synchronous belt and a pulley. The operating handle is fixed with the synchronousbelt, and the propulsion information is converted into rotation angle information throughthe synchronous belt group and then collected through angle sensor-A, connected to theleft end of the synchronous belt group. Angle sensor-B is connected with the extended endof the fixed shaft through a coupling to collect the rotation information. The resolutionof the angle sensor is 0.355◦, the independent linearity is ±0.1%, and the repeatability is0.01◦. The force sensation component is composed of a servo motor (RE10, Maxon Motor,Canton of Upper Walden, Switzerland) and a magnetic powder clutch (CD-HSY-5, Lanling,Haian, CN). The right end of the synchronous belt group is connected with the outputend of the magnetic powder clutch, and the input end of the magnetic powder clutch isconnected with the servo motor through the coupling. Image components are used todisplay intraoperative force–position information and assist doctors to judge the state ofthe guidewire tip.


on the premise of ensuring the doctor’s safety. In this process, the slave wire feeder

measures the proximal force of the guidewire in real time, and the master manipulator

forms a force sensation according to this, thus ensuring the safety of the surgery.

Master manipulator

Control system

Slave wire feeder

DSA

Computer

PatientDoctor

Forcefeedback

Operating

Master side Slave side

Medicalimage

Delivery guidewire

Forcesignal

Force feedback system



The master manipulator includes an operating component, a position detection com-

ponent, a force sensation component and an image component, as shown in Figure 2. The

operating component is composed of a spline shaft, operating handle, fixed shaft and

bearing block. The damped operating handle slides on the spline shaft. The fixed shaft is

attached to both sides of the spline shaft to limit its axial movement. The position detection

component consists of angle sensor-A, angle sensor-B (P2500, Miran Technology, Shen-

zhen, CN), a synchronous belt and a pulley. The operating handle is fixed with the syn-

chronous belt, and the propulsion information is converted into rotation angle infor-

mation through the synchronous belt group and then collected through angle sensor-A,

connected to the left end of the synchronous belt group. Angle sensor-B is connected with

the extended end of the fixed shaft through a coupling to collect the rotation information.

The resolution of the angle sensor is 0.355°, the independent linearity is ±0.1%, and the

repeatability is 0.01°. The force sensation component is composed of a servo motor (RE10,

Maxon Motor, Canton of Upper Walden, Switzerland) and a magnetic powder clutch (CD-

HSY-5, Lanling, Haian, CN). The right end of the synchronous belt group is connected

with the output end of the magnetic powder clutch, and the input end of the magnetic

powder clutch is connected with the servo motor through the coupling. Image compo-

nents are used to display intraoperative force–position information and assist doctors to

judge the state of the guidewire tip.

Servo motor

Magnetic powder clutch Pulley

Operating handle

Spline shaft

Angle sensor-A

Synchronous belt

Bearing block

Fixed shaft

Angle sensor-B

Image component

Figure 2. Virtual prototype of the master manipulator.

The operating handle decouples the propulsion and rotation of the guidewire, asshown in Figure 3. The connecting sleeve is fixed to the synchronous belt. The rotating

Appl. Sci. 2021, 11, 611 4 of 19

sleeve drives the spline shaft to rotate, and the two can rotate relative to each other.Therefore, the circumferential rotation of the spline shaft does not affect the axial propulsionof the operating handle. When the doctor delivers the guidewire, the middle finger andthumb squeeze the film sensor (Flexiforce-1lbsA201, Tekscan, Boston, US) to produce asignal to clamp the guidewire, while the forward thrust of the finger acts on the pressuresensor (MDL: SBT674-1 kg, Simbatouch, Guangzhou, CN) to produce a signal to propel theguidewire. The diaphragm sleeve and the rotating sleeve can slide relative to each otherthrough the sliding shaft. In the case of no external force, a certain pressure threshold canbe maintained through the micro spring and the adjusting bolt.



The operating handle decouples the propulsion and rotation of the guidewire, as

shown in Figure 3. The connecting sleeve is fixed to the synchronous belt. The rotating

sleeve drives the spline shaft to rotate, and the two can rotate relative to each other. There-

fore, the circumferential rotation of the spline shaft does not affect the axial propulsion of

the operating handle. When the doctor delivers the guidewire, the middle finger and

thumb squeeze the film sensor (Flexiforce-1lbsA201, Tekscan, Boston, US) to produce a

signal to clamp the guidewire, while the forward thrust of the finger acts on the pressure

sensor (MDL: SBT674-1 kg, Simbatouch, Guangzhou, CN) to produce a signal to propel

the guidewire. The diaphragm sleeve and the rotating sleeve can slide relative to each

other through the sliding shaft. In the case of no external force, a certain pressure thresh-

old can be maintained through the micro spring and the adjusting bolt.

Connecting sleeve

Micro spring

Pressure sensor

Sliding shaft

Film sensor

Rotating sleeve

End cover

Bearing Diaphragm sleeve

Spline shaft

(a) (b)

Figure 3. Virtual prototype (a) and section view (b) of the operation handle.

2.2. Slave Wire Feeder

The length of the guidewire is generally 1800~1950 mm, while the slave wire feeder

has a stroke of −200 mm. Therefore, the guidewire needs to be continuously propelled

through the coordination between fixed and moving components, as shown in Figure 4.

The Y-type valve holder is located at the front of the slave wire feeder to hold the medical

Y-type valve and adjust its angle. Fixed components include cams driven by a servo motor

(RE10, Maxon Motor, Canton of Upper Walden, Switzerland) and clamping blocks, which

are turned on when the guidewire is propelled forward and closed when the moving com-

ponent is returned. The moving component is used to drive the clamping component to

move on the sliding module. The clamping component can be used for the flexible clamp-

ing of guidewires of different types, and the clamping force can be adjusted or measured

in real time. The driving component is connected to the right shaft of the sliding module,

which is composed of a servo motor (28SYK43, UPTECH Robotics, CN), a synchronous

belt group, a fixed component and a frame to realize the propulsion or return of the mov-

ing component. The encoder (Modbus RTU, 8 bits/circle, RealwayTech, Beijing, CN) is

connected to the shaft at the left end of the sliding module for recording the position of

the guidewire. The axial propulsion error of the system is less than 0.2 mm.

Y-type valve holder

Fixed component

Moving component

Drive component

Sliding module

Encoder



The length of the guidewire is generally 1800~1950 mm, while the slave wire feeder hasa stroke of −200 mm. Therefore, the guidewire needs to be continuously propelled throughthe coordination between fixed and moving components, as shown in Figure 4. The Y-typevalve holder is located at the front of the slave wire feeder to hold the medical Y-type valveand adjust its angle. Fixed components include cams driven by a servo motor (RE10, MaxonMotor, Canton of Upper Walden, Switzerland) and clamping blocks, which are turnedon when the guidewire is propelled forward and closed when the moving component isreturned. The moving component is used to drive the clamping component to move onthe sliding module. The clamping component can be used for the flexible clamping ofguidewires of different types, and the clamping force can be adjusted or measured in realtime. The driving component is connected to the right shaft of the sliding module, which iscomposed of a servo motor (28SYK43, UPTECH Robotics, CN), a synchronous belt group, afixed component and a frame to realize the propulsion or return of the moving component.The encoder (Modbus RTU, 8 bits/circle, RealwayTech, Beijing, CN) is connected to theshaft at the left end of the sliding module for recording the position of the guidewire. Theaxial propulsion error of the system is less than 0.2 mm.



The operating handle decouples the propulsion and rotation of the guidewire, as

shown in Figure 3. The connecting sleeve is fixed to the synchronous belt. The rotating

sleeve drives the spline shaft to rotate, and the two can rotate relative to each other. There-

fore, the circumferential rotation of the spline shaft does not affect the axial propulsion of

the operating handle. When the doctor delivers the guidewire, the middle finger and

thumb squeeze the film sensor (Flexiforce-1lbsA201, Tekscan, Boston, US) to produce a

signal to clamp the guidewire, while the forward thrust of the finger acts on the pressure

sensor (MDL: SBT674-1 kg, Simbatouch, Guangzhou, CN) to produce a signal to propel

the guidewire. The diaphragm sleeve and the rotating sleeve can slide relative to each

other through the sliding shaft. In the case of no external force, a certain pressure thresh-

old can be maintained through the micro spring and the adjusting bolt.

Connecting sleeve

Micro spring

Pressure sensor

Sliding shaft

Film sensor

Rotating sleeve

End cover

Bearing Diaphragm sleeve

Spline shaft

(a) (b)



The length of the guidewire is generally 1800~1950 mm, while the slave wire feeder

has a stroke of −200 mm. Therefore, the guidewire needs to be continuously propelled

through the coordination between fixed and moving components, as shown in Figure 4.

The Y-type valve holder is located at the front of the slave wire feeder to hold the medical

Y-type valve and adjust its angle. Fixed components include cams driven by a servo motor

(RE10, Maxon Motor, Canton of Upper Walden, Switzerland) and clamping blocks, which

are turned on when the guidewire is propelled forward and closed when the moving com-

ponent is returned. The moving component is used to drive the clamping component to

move on the sliding module. The clamping component can be used for the flexible clamp-

ing of guidewires of different types, and the clamping force can be adjusted or measured

in real time. The driving component is connected to the right shaft of the sliding module,

which is composed of a servo motor (28SYK43, UPTECH Robotics, CN), a synchronous

belt group, a fixed component and a frame to realize the propulsion or return of the mov-

ing component. The encoder (Modbus RTU, 8 bits/circle, RealwayTech, Beijing, CN) is

connected to the shaft at the left end of the sliding module for recording the position of

the guidewire. The axial propulsion error of the system is less than 0.2 mm.

Y-type valve holder

Fixed component

Moving component

Drive component

Sliding module

Encoder

Figure 4. Virtual prototype of a slave wire feeder.

The moving component is shown in Figure 5. The lower bracket is fixed on thesliding module and drives the whole module to move in an axial direction. The clamping

Appl. Sci. 2021, 11, 611 5 of 19

component is used for the clamping guidewire. It is placed on the bearing bush of the upperbracket, and the whole component can be removed for convenient disinfection. A servomotor (RE8, Maxon Motor, Canton of Upper Walden, Switzerland) used for twisting isinstalled on the upper bracket. It is connected with the friction wheel through the coupling.The protection cover is used to ensure the friction wheel group is in close contact, so asto realize the rotational motion of the clamping component. The screw motor (DCX10L,Maxon Motor, Canton of Upper Walden, Switzerland) used for clamping is installed underthe clamping component. The screw nut is connected with the sliding fork. Driven bythe screw motor, the sliding fork can reciprocate and move on the slider, then propel thesliding sleeve of the clamping component to achieve flexible clamping of the guidewire.



The moving component is shown in Figure 5. The lower bracket is fixed on the sliding

module and drives the whole module to move in an axial direction. The clamping com-

ponent is used for the clamping guidewire. It is placed on the bearing bush of the upper

bracket, and the whole component can be removed for convenient disinfection. A servo

motor (RE8, Maxon Motor, Canton of Upper Walden, Switzerland) used for twisting is

installed on the upper bracket. It is connected with the friction wheel through the cou-

pling. The protection cover is used to ensure the friction wheel group is in close contact,

so as to realize the rotational motion of the clamping component. The screw motor

(DCX10L, Maxon Motor, Canton of Upper Walden, Switzerland) used for clamping is in-

stalled under the clamping component. The screw nut is connected with the sliding fork.

Driven by the screw motor, the sliding fork can reciprocate and move on the slider, then

propel the sliding sleeve of the clamping component to achieve flexible clamping of the

guidewire.

Clamping component

Bearing bush

Pin shaft

Servo motor

Slideway

Slider

Sliding fork

Screw nut

Upper bracket

Lower bracket

Connecting shaft

Screw motor

Friction wheel

Coupling

Magnet

Protection cover

Figure 5. Moving component.

The internal structure of the clamping component is shown in Figure 6. A fixed sleeve

and a copper bush are installed on the hollow shaft, and the end is mounted with a driven

friction wheel. The sliding sleeve can slide on the hollow shaft and propel the clamping

blocks to move on the wedge clamping sleeve through the push rod to realize the clamp-

ing of the guidewire. Due the action of the spring, the clamping component is in a nor-

mally open state. Only when the screw motor propels the sliding fork will the guidewire

be clamped, and the clamping force will be adjusted in real time. The clamping sensor

(MDL: SBT674-1 kg, Simbatouch, Guangzhou, CN) is embedded in the lower clamping

block to measure the clamping force during operation and display it on the image com-

ponent of the master manipulator.

Copper bush

Friction wheel

Lock ring

Sliding sleeve

Spring

Hollow shaft

Fixed sleeve

Push rod

Clamping sensor

Clamping block

Clamping sleeve


The internal structure of the clamping component is shown in Figure 6. A fixed sleeveand a copper bush are installed on the hollow shaft, and the end is mounted with a drivenfriction wheel. The sliding sleeve can slide on the hollow shaft and propel the clampingblocks to move on the wedge clamping sleeve through the push rod to realize the clampingof the guidewire. Due the action of the spring, the clamping component is in a normallyopen state. Only when the screw motor propels the sliding fork will the guidewire beclamped, and the clamping force will be adjusted in real time. The clamping sensor (MDL:SBT674-1 kg, Simbatouch, Guangzhou, CN) is embedded in the lower clamping block tomeasure the clamping force during operation and display it on the image component ofthe master manipulator.



The moving component is shown in Figure 5. The lower bracket is fixed on the sliding

module and drives the whole module to move in an axial direction. The clamping com-

ponent is used for the clamping guidewire. It is placed on the bearing bush of the upper

bracket, and the whole component can be removed for convenient disinfection. A servo

motor (RE8, Maxon Motor, Canton of Upper Walden, Switzerland) used for twisting is

installed on the upper bracket. It is connected with the friction wheel through the cou-

pling. The protection cover is used to ensure the friction wheel group is in close contact,

so as to realize the rotational motion of the clamping component. The screw motor

(DCX10L, Maxon Motor, Canton of Upper Walden, Switzerland) used for clamping is in-

stalled under the clamping component. The screw nut is connected with the sliding fork.

Driven by the screw motor, the sliding fork can reciprocate and move on the slider, then

propel the sliding sleeve of the clamping component to achieve flexible clamping of the

guidewire.

Clamping component

Bearing bush

Pin shaft

Servo motor

Slideway

Slider

Sliding fork

Screw nut

Upper bracket

Lower bracket

Connecting shaft

Screw motor

Friction wheel

Coupling

Magnet

Protection cover


The internal structure of the clamping component is shown in Figure 6. A fixed sleeve

and a copper bush are installed on the hollow shaft, and the end is mounted with a driven

friction wheel. The sliding sleeve can slide on the hollow shaft and propel the clamping

blocks to move on the wedge clamping sleeve through the push rod to realize the clamp-

ing of the guidewire. Due the action of the spring, the clamping component is in a nor-

mally open state. Only when the screw motor propels the sliding fork will the guidewire

be clamped, and the clamping force will be adjusted in real time. The clamping sensor

(MDL: SBT674-1 kg, Simbatouch, Guangzhou, CN) is embedded in the lower clamping

block to measure the clamping force during operation and display it on the image com-

ponent of the master manipulator.

Copper bush

Friction wheel

Lock ring

Sliding sleeve

Spring

Hollow shaft

Fixed sleeve

Push rod

Clamping sensor

Clamping block

Clamping sleeve

Figure 6. Clamping component.

2.3. Force Feedback System

In order to realize the force feedback and the flexible clamping, for the master manip-ulator, the servo motor and magnetic powder clutch combination scheme is adopted to

Appl. Sci. 2021, 11, 611 6 of 19

provide the required force sensation. The relative pressure between the operating handleand the hand is maintained by adjusting the speed of the servo motor. According to the slipcharacteristic of the magnetic powder clutch, when the input excitation current remainsunchanged, the transferred torque is not affected by the rotational speed difference betweenthe main and the driven parts. Therefore, when the magnetic powder clutch provides aforce sensation, it can weaken the shaking phenomenon caused by braking torque. A filmsensor and a pressure sensor are set in the operating handle to detect the clamping forceand the propulsion force exerted by the doctor, respectively, as shown in Figure 7.




In order to realize the force feedback and the flexible clamping, for the master ma-

nipulator, the servo motor and magnetic powder clutch combination scheme is adopted

to provide the required force sensation. The relative pressure between the operating han-

dle and the hand is maintained by adjusting the speed of the servo motor. According to

the slip characteristic of the magnetic powder clutch, when the input excitation current

remains unchanged, the transferred torque is not affected by the rotational speed differ-

ence between the main and the driven parts. Therefore, when the magnetic powder clutch

provides a force sensation, it can weaken the shaking phenomenon caused by braking

torque. A film sensor and a pressure sensor are set in the operating handle to detect the

clamping force and the propulsion force exerted by the doctor, respectively, as shown in

Figure 7.

Film sensor

Magnetic powder clutch+Servo motor

Doctor

Master manipulator

Guidewire

Resistance detection mechanism

Clamping component

Slave wire feeder

Force sensation

Twist Clamping

PropulsionPressure sensor

Regulation

Detection

Clamping sensor

Collection

Figure 7. Force perception feedback system.

For the slave wire feeder, the actual clamping force of the guidewire is collected

through the clamping sensor. When the guidewire tip touches the blood vessel and is sub-

jected to force, the resistance detection mechanism will amplify the force through the lever

principle and convert it into positive pressure against the resistance sensor (MDL: SBT674-

5 kg, Simbatouch, Guangzhou, CN). The spring is set between the upper and lower

bracket to balance the effect of gravity on the mechanism itself. The lever arm can be

changed by adjusting the position of the resistance sensor to regulate the amplification

ratio, which can be set to two here, as shown in Figure 8.

Guidewire Upper bracket

Lower bracket

Connecting shaft

Resistance sensor

Spring

Figure 8. Resistance detection mechanism.

3. Dynamic Analysis

3.1. Dynamical Model of Propulsion


For the slave wire feeder, the actual clamping force of the guidewire is collectedthrough the clamping sensor. When the guidewire tip touches the blood vessel and issubjected to force, the resistance detection mechanism will amplify the force through thelever principle and convert it into positive pressure against the resistance sensor (MDL:SBT674-5 kg, Simbatouch, Guangzhou, CN). The spring is set between the upper and lowerbracket to balance the effect of gravity on the mechanism itself. The lever arm can bechanged by adjusting the position of the resistance sensor to regulate the amplificationratio, which can be set to two here, as shown in Figure 8.




In order to realize the force feedback and the flexible clamping, for the master ma-

nipulator, the servo motor and magnetic powder clutch combination scheme is adopted

to provide the required force sensation. The relative pressure between the operating han-

dle and the hand is maintained by adjusting the speed of the servo motor. According to

the slip characteristic of the magnetic powder clutch, when the input excitation current

remains unchanged, the transferred torque is not affected by the rotational speed differ-

ence between the main and the driven parts. Therefore, when the magnetic powder clutch

provides a force sensation, it can weaken the shaking phenomenon caused by braking

torque. A film sensor and a pressure sensor are set in the operating handle to detect the

clamping force and the propulsion force exerted by the doctor, respectively, as shown in

Figure 7.

Film sensor

Magnetic powder clutch+Servo motor

Doctor

Master manipulator

Guidewire

Resistance detection mechanism

Clamping component

Slave wire feeder

Force sensation

Twist Clamping

PropulsionPressure sensor

Regulation

Detection

Clamping sensor

Collection


For the slave wire feeder, the actual clamping force of the guidewire is collected

through the clamping sensor. When the guidewire tip touches the blood vessel and is sub-

jected to force, the resistance detection mechanism will amplify the force through the lever

principle and convert it into positive pressure against the resistance sensor (MDL: SBT674-

5 kg, Simbatouch, Guangzhou, CN). The spring is set between the upper and lower

bracket to balance the effect of gravity on the mechanism itself. The lever arm can be

changed by adjusting the position of the resistance sensor to regulate the amplification

ratio, which can be set to two here, as shown in Figure 8.

Guidewire Upper bracket

Lower bracket

Connecting shaft

Resistance sensor

Spring


3. Dynamic Analysis

3.1. Dynamical Model of Propulsion


3. Dynamic Analysis3.1. Dynamical Model of Propulsion

For the motion diagram of the propulsion of the guidewire, as shown in Figure 9, itstransmission principle is as follows. The servo motor, as the power source of the slave wirefeeder, drives the synchronous belt group through elastic coupling-1. The synchronous beltgroup converts the driving torque of the servo motor into the driving force of the movingload, and the moving load fixed on the synchronous belt realizes the propulsion of theguidewire with the positive and negative rotation of the motor.

Appl. Sci. 2021, 11, 611 7 of 19


For the motion diagram of the propulsion of the guidewire, as shown in Figure 9, its

transmission principle is as follows. The servo motor, as the power source of the slave

wire feeder, drives the synchronous belt group through elastic coupling-1. The synchro-

nous belt group converts the driving torque of the servo motor into the driving force of

the moving load, and the moving load fixed on the synchronous belt realizes the propul-

sion of the guidewire with the positive and negative rotation of the motor.

F1

FN

Ff

Tp4 Tp3

T2 T1

Tm

Tp2 Tp1

Xs

FL

Figure 9. Motion diagram of propulsion.

According to the transmission process of the propulsive motion of the slave wire

feeder, the dynamic equation of the components in the transmission mechanism is estab-

lished in turn, ignoring the influence of the friction between components and nonlinear

factors such as deformation in the motion process. The dynamic equation of the output

side of the servo motor is

𝑇𝑚 = 𝐽𝑠𝑑2𝜃𝑚𝑑𝑡2

+ 𝐵𝑠𝑑𝜃𝑚𝑑𝑡

+ 𝑇1 (1)

where 𝑇𝑚 is the driving torque of the propulsion motor, 𝐽𝑠 is the inertia of the propulsion

motor and its reducer, 𝜃𝑚 is the angle of the propulsion motor output shaft, 𝐵𝑠 is the

damping coefficient of the propulsion motor and 𝑇1 is the driving torque of elastic cou-

pling-1.

The dynamic equation from coupling-1 to the moving load is

𝑇1 = 𝐽1𝑑2𝜃𝑚𝑑𝑡2

+ 𝐽𝑝1𝑑2𝜃𝑚𝑑𝑡2


+ 𝐽2𝑑2𝜃𝑚𝑑𝑡2



+ 𝐹𝐿 ·𝑑

2 (2)

where 𝐽1 is the inertia of elastic coupling-1, 𝐽2 is the inertia of elastic coupling-2, 𝐽𝑝 is the

inertia of the pulley, 𝐹𝐿 is the moving load and 𝑑 is the diameter of the pulley.

The moving load fixed on the synchronous belt group is analyzed as follows:

𝐹𝐿 = 𝑚𝑡

𝑑2𝑥𝑠𝑑𝑡2

+ 𝜇𝑣𝑑𝑥𝑠𝑑𝑡

+ 𝜇𝑐𝑚𝑡𝑔 (3)

where 𝑚𝑡 is the mass of the slider and the object mounted on it, 𝜇𝑣 and 𝜇𝑐 are the sticky

coefficient of friction and Cullen coefficient of friction, respectively, and 𝑥𝑠 is the axial

displacement of the guidewire.

The relationship between the axial displacement of the guidewire and the angle of

the servo motor output shaft is as follows:

𝑥𝑆 = 𝜃𝑚 ·𝑑

2 (4)

Figure 9. Motion diagram of propulsion.

According to the transmission process of the propulsive motion of the slave wire feeder,the dynamic equation of the components in the transmission mechanism is establishedin turn, ignoring the influence of the friction between components and nonlinear factorssuch as deformation in the motion process. The dynamic equation of the output side of theservo motor is

Tm = Jsd2θm

dt2 + Bsdθm

dt+ T1 (1)

where Tm is the driving torque of the propulsion motor, Js is the inertia of the propulsionmotor and its reducer, θm is the angle of the propulsion motor output shaft, Bs is the dampingcoefficient of the propulsion motor and T1 is the driving torque of elastic coupling-1.

The dynamic equation from coupling-1 to the moving load is

T1 = J1d2θm

dt2 + Jp1d2θm

dt2 + Jp2d2θm

dt2 + J2d2θm

dt2 + Jp3d2θm

dt2 + Jp4d2θm

dt2 + FL·d2

(2)

where J1 is the inertia of elastic coupling-1, J2 is the inertia of elastic coupling-2, Jp is theinertia of the pulley, FL is the moving load and d is the diameter of the pulley.

The moving load fixed on the synchronous belt group is analyzed as follows:

FL = mtd2xs

dt2 + µvdxs

dt+ µcmtg (3)

where mt is the mass of the slider and the object mounted on it, µv and µc are the stickycoefficient of friction and Cullen coefficient of friction, respectively, and xs is the axialdisplacement of the guidewire.

The relationship between the axial displacement of the guidewire and the angle of theservo motor output shaft is as follows:

xS = θm·d2

(4)

The above formula, joined with prior equations and values, yields

Tm = 2d

(Js + J1 + J2 + Jp1 + Jp2 + Jp3 + Jp4 + mt · d2

4

)d2xsdt2

+(

Bs · 2d + µv · d

2

)dxsdt + µcmtg· d2

(5)

For simplified modeling, µcmtg· d2 is labeled as a disturbance.

Appl. Sci. 2021, 11, 611 8 of 19

3.2. Dynamical Model of Rotation

The principle of the rotation of the guidewire is similar to the propulsion, and itsmotion diagram is shown in Figure 10. The servo motor, as the power source for rotating,is fixed with elastic coupling-3 and drives the clamping component to rotate through thefriction wheel group.



𝑇𝑚 =2

𝑑(𝐽𝑠 + 𝐽1 + 𝐽2 + 𝐽𝑝1 + 𝐽𝑝2 + 𝐽𝑝3 + 𝐽𝑝4 +𝑚𝑡 ⋅

𝑑2

4)𝑑2𝑥𝑠𝑑𝑡2

+(𝐵𝑠 ⋅2

𝑑+ 𝜇𝑣 ⋅

𝑑

2)𝑑𝑥𝑠𝑑𝑡

+ 𝜇𝑐𝑚𝑡𝑔 ·𝑑

2

(5)

For simplified modeling, 𝜇𝑐𝑚𝑡𝑔 ·𝑑

2 is labeled as a disturbance.

3.2. Dynamical Model of Rotation

The principle of the rotation of the guidewire is similar to the propulsion, and its

motion diagram is shown in Figure 10. The servo motor, as the power source for rotating,

is fixed with elastic coupling-3 and drives the clamping component to rotate through the

friction wheel group.

Txm1

T3 Txp1

Tx Txp2

θxm2

θxm1

Figure 10. Motion diagram of rotation.

According to the transmission process of the rotating motion of the slave wire feeder,

the dynamics equations of the relevant components are established in turn, ignoring the

influence of nonlinear factors such as deformation and vibration of the friction wheel

group during the motion process. The dynamic equation of the output side of the servo

motor is

𝑇𝑥𝑚1 = 𝐽𝑥𝑠𝑑2𝜃𝑥𝑚1𝑑𝑡2

+ 𝐵𝑥𝑠𝑑𝜃𝑥𝑚1𝑑𝑡

+ 𝑇3 (6)

where 𝑇𝑥𝑚1 is the driving torque of the rotation motor, 𝐽𝑥𝑠 is the inertia of the rotation

motor and its reducer, 𝜃𝑥𝑚1 is the angle of the rotation motor output shaft, 𝐵𝑥𝑠 is the

damping coefficient of the rotation motor and 𝑇3 is the driving torque of elastic coupling-

3.

The dynamic equation from elastic coupling-3 to the clamping component is as fol-

lows:

𝑇3 = 𝐽3𝑑2𝜃𝑥𝑚1𝑑𝑡2

+𝐽𝑥𝑝1𝑑2𝜃𝑥𝑚1𝑑𝑡2

+ 𝐽𝑥𝑝2𝑑2𝜃𝑥𝑚2𝑑𝑡2

+ 𝑇𝑥 (7)

where 𝐽3 is the inertia of elastic coupling-3, 𝐽𝑥𝑝 is the inertia of the friction wheel, 𝜃𝑥𝑚2

is the angle of the clamping component and 𝑇x is the torque of the clamping component.

The torque of the clamping component is analyzed as follows:

𝑇𝑥 = 𝐽𝑥𝑑2𝜃𝑥𝑚2𝑑𝑡2

+ 𝐹𝑓 ·𝑑𝑥2

(8)

Figure 10. Motion diagram of rotation.

According to the transmission process of the rotating motion of the slave wire feeder,the dynamics equations of the relevant components are established in turn, ignoring theinfluence of nonlinear factors such as deformation and vibration of the friction wheel groupduring the motion process. The dynamic equation of the output side of the servo motor is

Txm1 = Jxsd2θxm1

dt2 + Bxsdθxm1

dt+ T3 (6)

where Txm1 is the driving torque of the rotation motor, Jxs is the inertia of the rotation motorand its reducer, θxm1 is the angle of the rotation motor output shaft, Bxs is the dampingcoefficient of the rotation motor and T3 is the driving torque of elastic coupling-3.

The dynamic equation from elastic coupling-3 to the clamping component is as follows:

T3 = J3d2θxm1

dt2 + Jxp1d2θxm1

dt2 + Jxp2d2θxm2

dt2 + Tx (7)

where J3 is the inertia of elastic coupling-3, Jxp is the inertia of the friction wheel, θxm2 isthe angle of the clamping component and Tx is the torque of the clamping component.

The torque of the clamping component is analyzed as follows:

Tx = Jxd2θxm2

dt2 + Ff ·dx

2(8)

where Jx is the inertia of the clamping component, Ff is the load on the clamping componentand dx is the diameter of the clamping component.

The relationship between the rotation angle of the clamping component and therotation angle of the output shaft of the servo motor is as follows:

θxm2 = θxm1·i (9)


Txm1 =1i(

Jxs + J3 + Jxp1)d2θxm2

dt2 +(

Jxp2 + Jx)d2θxm2

dt2 +Bxs

i·dθxm2

dt+ µxvmxtg·

dx

2(10)

Appl. Sci. 2021, 11, 611 9 of 19

For simplified modeling, µxvmxtg· dx2 is labeled as a disturbance.

4. Design of the Control System

Due to the infinite freedom of the guidewire and the nonlinear uncertain disturbancecaused by the flexible components in the mechanism, an accurate dynamical model cannotbe established. In addition, the friction force of the mechanism itself and intraoperativeshaking of the doctor’s hand will also cause nonlinear interference to the system. Thesedisturbances not only affect the tracking accuracy of the master–slave position, but alsohave great influence on the acquisition of force signals. Therefore, the control system of theVISR must be able to eliminate the above effects to a certain extent, while the simple closed-loop control system and conventional PID controller cannot meet these requirements. Inthis paper, the fuzzy control strategy is added to the PID controller to make it have on-linetuning parameters and anti-interference functions.

4.1. Design of the Traditional Controller and the PID Controller

The above model is static, but considering that the interaction of various factors inthe actual wire feeding process is dynamic, according to the fuzzy PID control theory, theconventional PID control system of the mechanism should be set up, and then the initialvalues of ∆kp, ∆ki and ∆kd in the system can be obtained. In Section 3.1, the dynamicequation of the propulsion was obtained, which was substituted into the data and a Laplacetransform was performed. Then, the second-order transfer function of the system wasobtained as follows:

G(s) =1

0.01186s2 + 0.09602s(11)

The traditional controller and PID controller were built in the Simulink environmentof the MATLAB software (2016a, MathWorks, Natick, MA, US), as shown in Figures 11 and12. The traditional controller was a simple closed-loop control system, in which negativefeedback was added to the transfer function of the system. On this basis, the PID modulein the Simulink library was called upon and added to the aforementioned system to obtainthe PID controller. The PID parameters were adjusted by the empirical trial and errormethod to achieve stability of the system, and the final parameters were determined asKp = 1.5, Ki = 0.2 and Kd = 0.172.


Figure 11. Control system comparison schematic.

Figure 12. Conventional PID controller.

4.2. Design of the Two-Dimensional Fuzzy PID Controller

The input value of the fuzzy controller designed in this paper included the deviation

(𝑒) and deviation change rate (𝑒𝑐 = 𝑑𝑢/𝑑𝑡). Since they were two components of 𝑥, the

controller was regarded as a two-dimensional fuzzy PID controller, and its principle is

shown in Figure 13.

du/dt

FuzzificationD/F

Fuzzy Inference Engine

Fuzzy Rule Base

DefuzzificationF/D

x e

ec

K

FC

Figure 13. Principle of a two-dimensional fuzzy controller.

The fuzzy controller can be expressed as a mapping from the input to the output:

𝛥𝐾 = 𝐹𝐶(𝑒, 𝑒𝑐) (12)

where 𝐹𝐶 is the fuzzy controller, and the input (𝑒, 𝑒𝑐) and the outputs 𝛥𝑘𝑝, 𝛥𝑘𝑖 and

𝛥𝑘𝑑, by selecting the appropriate membership functions 𝜇(𝑥), transform for a fuzzy sub-

set of the fuzzy theory domain. Its basic theoretical domain is 𝑈 = [−1,1], the fuzzy the-

oretical domain is 𝑁 = [−0.1,0.1] and the quantization factor is 𝑘 = 10. Since the basic

domain of 𝑥 is continuous, its membership function 𝜇(𝑥) is triangular (Equation (13)),

and the transition adjustment of boundary is Gaussian (Equation (14)):


Appl. Sci. 2021, 11, 611 10 of 19








shown in Figure 13.

du/dt

FuzzificationD/F


Fuzzy Rule Base

DefuzzificationF/D

x e

ec

K

FC



𝛥𝐾 = 𝐹𝐶(𝑒, 𝑒𝑐) (12)









The input value of the fuzzy controller designed in this paper included the deviation(e) and deviation change rate (ec = du/dt). Since they were two components of x, thecontroller was regarded as a two-dimensional fuzzy PID controller, and its principle isshown in Figure 13.








shown in Figure 13.

du/dt

FuzzificationD/F


Fuzzy Rule Base

DefuzzificationF/D

x e

ec

K

FC



𝛥𝐾 = 𝐹𝐶(𝑒, 𝑒𝑐) (12)









∆K = FC(e, ec) (12)

where FC is the fuzzy controller, and the input (e, ec) and the outputs ∆kp, ∆ki and ∆kd,by selecting the appropriate membership functions µ(x), transform for a fuzzy subset ofthe fuzzy theory domain. Its basic theoretical domain is U = [−1, 1], the fuzzy theoreticaldomain is N = [−0.1, 0.1] and the quantization factor is k = 10. Since the basic domain of xis continuous, its membership function µ(x) is triangular (Equation (13)), and the transitionadjustment of boundary is Gaussian (Equation (14)):

µ(x) =

0 x ≤ ax−ab−a a ≤ x ≤ bc−xc−b b ≤ x ≤ c0 x ≥ c

(13)

µ(x) = e−(x−cF)2/ω (14)

The fuzzy subsets A1 and A2 of the input quantities were determined as {MinusBig, Minus Medium, Minus Small, Zero, Plus Small, Plus Medium, Plus Big}, and thecorresponding symbols were {NB, NM, NS, ZO, PS, PM, PB}. The fuzzy subset B1, B2and B3 of the output quantities were determined as {Minus Big, Minus Medium, MinusSmall, Zero, Plus Small, Plus Medium, Plus Big}, and the corresponding symbols were

Appl. Sci. 2021, 11, 611 11 of 19

{NB, NM, NS, ZO, PS, PM, PB}. The design of the fuzzy rules, based on expert experiencemethod, is

R(l) : IF e is Ai1 and ec is Aj

2, THEN ∆Kp is Bl1 ∆Ki is Bl

2 ∆Kd is Bl3 (15)

The resulting fuzzy control surface of the outputs ∆kp, ∆ki and ∆kd is shown inFigure 14.


𝜇(𝑥) =

{

0 𝑥 ≤ 𝑎𝑥 − 𝑎

𝑏 − 𝑎𝑎 ≤ 𝑥 ≤ 𝑏

𝑐 − 𝑥

𝑐 − 𝑏𝑏 ≤ 𝑥 ≤ 𝑐

0 𝑥 ≥ 𝑐

(13)

𝜇(𝑥) = 𝑒−(𝑥−𝑐𝐹)2/𝜔 (14)

The fuzzy subsets 𝐴1 and 𝐴2 of the input quantities were determined as {Minus Big,

Minus Medium, Minus Small, Zero, Plus Small, Plus Medium, Plus Big}, and the corre-

sponding symbols were {𝑁𝐵,𝑁𝑀,𝑁𝑆, 𝑍𝑂, 𝑃𝑆, 𝑃𝑀, 𝑃𝐵}. The fuzzy subset 𝐵1, 𝐵2 and 𝐵3

of the output quantities were determined as {Minus Big, Minus Medium, Minus Small,

Zero, Plus Small, Plus Medium, Plus Big}, and the corresponding symbols were

{𝑁𝐵,𝑁𝑀,𝑁𝑆, 𝑍𝑂, 𝑃𝑆, 𝑃𝑀, 𝑃𝐵}. The design of the fuzzy rules, based on expert experience

method, is

𝑅(𝑙): IF 𝑒 is 𝐴1𝑖 and 𝑒𝑐 is 𝐴2

𝑗, THEN 𝛥𝐾𝑝 is 𝐵1

𝑙 𝛥𝐾𝑖 is 𝐵2𝑙 𝛥𝐾𝑑 is 𝐵3

𝑙 (15)

The resulting fuzzy control surface of the outputs 𝛥𝑘𝑝, 𝛥𝑘𝑖 and 𝛥𝑘𝑑 is shown in

Figure 14.

5

0

-52

0-2 -2 -1 0

1 2

eec

Kp

5

0

-52

0-2 -2 -1 0

1 2

eec

Ki

5

0

-52

0-2 -2 -1 0

1 2

eec

Kd

(a) (b) (c)

Figure 14. The fuzzy control surface of the outputs 𝛥𝑘𝑝 (a), 𝛥𝑘𝑖 (b) and 𝛥𝑘𝑑 (c).

The fuzzy controller outputs a fuzzy set. According to the formula 𝑘 = 𝑢/𝑛, the

fuzzy domain is transformed into the basic domain. The area barycenter method is used

to de-fuzzy the parameters:

𝑧0 = df(𝑧) =∫ 𝑧𝜇(𝑧) 𝑑 𝑧𝑏

𝑎

∫ 𝜇(𝑧) 𝑑 𝑧𝑏

𝑎

(16)

where 𝑧0 is the exact value, 𝑧 is the fuzzy value, 𝜇(𝑧) is the membership function and

[𝑎, 𝑏] is the theoretical domain.

Hence, the scale factor for de-fuzzification can be obtained: 𝛥𝑘𝑝 = 0.5, 𝛥𝑘𝑖 = 20 and

𝛥𝑘𝑑 = 0.2.

4.3. Control System Simulation

According to the analysis in the first two sections, the fuzzy PID controller in Figure

11 was built through the Simulink environment of MATLAB. The sine and step signals

were selected to compare their follow-up performance, and the random interference sig-

nals were added to observe their anti-interference ability, as shown in Figure 15.

Figure 14. The fuzzy control surface of the outputs ∆kp (a), ∆ki (b) and ∆kd (c).

The fuzzy controller outputs a fuzzy set. According to the formula k = u/n, thefuzzy domain is transformed into the basic domain. The area barycenter method is used tode-fuzzy the parameters:

z0 = df(z) =

∫ ba zµ(z)dz∫ ba µ(z)dz

(16)

where z0 is the exact value, z is the fuzzy value, µ(z) is the membership function and [a, b]is the theoretical domain.

Hence, the scale factor for de-fuzzification can be obtained: ∆kp = 0.5, ∆ki = 20 and∆kd = 0.2.

4.3. Control System Simulation

According to the analysis in the first two sections, the fuzzy PID controller in Figure 11was built through the Simulink environment of MATLAB. The sine and step signals wereselected to compare their follow-up performance, and the random interference signalswere added to observe their anti-interference ability, as shown in Figure 15.


Figure 15. Fuzzy PID controller.

The Bode diagram of the control system was drawn by MATLAB, and the stability

margin was obtained, as shown in Figure 16. The phase margin (PM) of the traditional

controller was 28.5°, the PM of the PID controller was 30.7°, and the PM of the fuzzy PID

controller was 67.1°. The phase frequency characteristics of the three controllers did not

cross the −180° line, so the system must have been stable, and the fuzzy PID controller had

the best stability.

PID Traditional

Fuzzy PID

Mag

nit

ud

e (d

B)

Ph

ase

(deg

)

-90-100

-50

0

50

100

150

200

-120

-180

-150

10-3 10-2 10-1 100 101 102 103 104

Frequency (rad/s)

Figure 16. Bode diagram of the control system.

The preset signal sampling period was 0.001 s. The response curves of the three con-

trollers under step signals are shown in Figure 17, and the relevant parameters of the con-

trollers are listed in Table 1. It can be seen from Figure 17 and Table 1 that the two-dimen-

sional fuzzy PID controller designed in this paper had a faster response speed, lower over-

sets and a smaller steady state error than the traditional PID controller under the same

interference signal, and it was less affected by the interference signal.


The Bode diagram of the control system was drawn by MATLAB, and the stabilitymargin was obtained, as shown in Figure 16. The phase margin (PM) of the traditionalcontroller was 28.5◦, the PM of the PID controller was 30.7◦, and the PM of the fuzzy PIDcontroller was 67.1◦. The phase frequency characteristics of the three controllers did not

Appl. Sci. 2021, 11, 611 12 of 19

cross the −180◦ line, so the system must have been stable, and the fuzzy PID controllerhad the best stability.



The Bode diagram of the control system was drawn by MATLAB, and the stability

margin was obtained, as shown in Figure 16. The phase margin (PM) of the traditional

controller was 28.5°, the PM of the PID controller was 30.7°, and the PM of the fuzzy PID

controller was 67.1°. The phase frequency characteristics of the three controllers did not

cross the −180° line, so the system must have been stable, and the fuzzy PID controller had

the best stability.

PID Traditional

Fuzzy PID

Mag

nit

ud

e (d

B)

Ph

ase

(deg

)

-90-100

-50

0

50

100

150

200

-120

-180

-150

10-3 10-2 10-1 100 101 102 103 104

Frequency (rad/s)


The preset signal sampling period was 0.001 s. The response curves of the three con-

trollers under step signals are shown in Figure 17, and the relevant parameters of the con-

trollers are listed in Table 1. It can be seen from Figure 17 and Table 1 that the two-dimen-

sional fuzzy PID controller designed in this paper had a faster response speed, lower over-

sets and a smaller steady state error than the traditional PID controller under the same

interference signal, and it was less affected by the interference signal.


The preset signal sampling period was 0.001 s. The response curves of the threecontrollers under step signals are shown in Figure 17, and the relevant parameters ofthe controllers are listed in Table 1. It can be seen from Figure 17 and Table 1 that thetwo-dimensional fuzzy PID controller designed in this paper had a faster response speed,lower oversets and a smaller steady state error than the traditional PID controller underthe same interference signal, and it was less affected by the interference signal.


Ideal curve

PID Traditional

Re

spo

nse

(m

m)

Fuzzy PID

0 0.5 1 1.5 2 2.5 3 3.5 40

0.2

0.4

0.6

0.8

1

0.94

0.98

1.02

1.2

1.06

Time (s)

1.4

1.1

0.75 0.85 0.95 1.05

0.96

1

1.04

1.08

0.922.8 2.9 3 3.1 3.2

Figure 17. The response of the step signal.

Table 1. Controller performance parameters table.

Controller Rise Time (s) Maximum Deviation (mm) Mean Deviation (mm)

Traditional controller 0.44 0.3778 0.1153

PID controller 0.13 0.1222 0.0724

Fuzzy PID controller 0.12 0.0400 0.0147

5. Experimental Validation

In order to verify the performance of the designed VISR, first, the anti-interference

ability of the fuzzy PID controller was analyzed through experimentation. Then, two ex-

periments were designed to evaluate the force feedback quality of the VISR and its ability

to control the clamping force in real time. Finally, the experiments showed that the VISR

can safely and effectively assist doctors to deliver the guidewire accurately.

5.1. Fuzzy PID Controller Anti-Interference Experiment

The experimental device is shown in Figure 18. The PID controller and fuzzy PID

controller designed in Chapter 4 were used to operate the VISR. The operator pushed the

operating handle of the main manipulator, and after the slave wire feeder ran to a prede-

termined position, random interference was applied to the encoder (related information

was introduced in Section 2.2). The response of the system and the time needed to adjust

the interference were recorded under the actions of the two controllers. The experimental

results are shown in Figure 19.

Apply interference to the encoder

Slave wire feeder

Master manipulator

Figure 18. Anti-interference experimental device.





PID controller 0.13 0.1222 0.0724


Appl. Sci. 2021, 11, 611 13 of 19


In order to verify the performance of the designed VISR, first, the anti-interferenceability of the fuzzy PID controller was analyzed through experimentation. Then, twoexperiments were designed to evaluate the force feedback quality of the VISR and its abilityto control the clamping force in real time. Finally, the experiments showed that the VISRcan safely and effectively assist doctors to deliver the guidewire accurately.


The experimental device is shown in Figure 18. The PID controller and fuzzy PIDcontroller designed in Chapter 4 were used to operate the VISR. The operator pushed theoperating handle of the main manipulator, and after the slave wire feeder ran to a prede-termined position, random interference was applied to the encoder (related informationwas introduced in Section 2.2). The response of the system and the time needed to adjustthe interference were recorded under the actions of the two controllers. The experimentalresults are shown in Figure 19.


Ideal curve

PID Traditional

Re

spo

nse

(m

m)

Fuzzy PID

0 0.5 1 1.5 2 2.5 3 3.5 40

0.2

0.4

0.6

0.8

1

0.94

0.98

1.02

1.2

1.06

Time (s)

1.4

1.1

0.75 0.85 0.95 1.05

0.96

1

1.04

1.08

0.922.8 2.9 3 3.1 3.2





PID controller 0.13 0.1222 0.0724



In order to verify the performance of the designed VISR, first, the anti-interference

ability of the fuzzy PID controller was analyzed through experimentation. Then, two ex-

periments were designed to evaluate the force feedback quality of the VISR and its ability

to control the clamping force in real time. Finally, the experiments showed that the VISR

can safely and effectively assist doctors to deliver the guidewire accurately.


The experimental device is shown in Figure 18. The PID controller and fuzzy PID

controller designed in Chapter 4 were used to operate the VISR. The operator pushed the

operating handle of the main manipulator, and after the slave wire feeder ran to a prede-

termined position, random interference was applied to the encoder (related information

was introduced in Section 2.2). The response of the system and the time needed to adjust

the interference were recorded under the actions of the two controllers. The experimental

results are shown in Figure 19.

Apply interference to the encoder

Slave wire feeder

Master manipulator

Figure 18. Anti-interference experimental device. Figure 18. Anti-interference experimental device.


Ideal curve

PID

Res

po

nse

(m

m)

Fuzzy PID

0

50

100

150

200

210

214

218

250

222

Time (s)

300

3 3.5 4

205

215

225

19519.5 20.5 21.5 22.5 23.5

2 5 8 101 3 4 6 7 9 110 14 17 20 2213 15 16 18 19 2112

4.5210

214

218

222

8.5 9 9.5 10 24.5

Amplitude: 10 Amplitude: 8 Amplitude: 12

Figure 19. The responses of the two controllers to interference signals.

The analysis showed that that the response speed of the fuzzy PID controller was

faster. The conventional PID controller had a maximum amplitude of −18 mm and an av-

erage amplitude of 3.45 mm, while the fuzzy PID controller had a maximum amplitude

of −13 mm and an average amplitude of 2.37 mm. When the amplitude was −8 mm, the

adjustment time of the fuzzy PID controller was 43.75% faster than that of the ordinary

PID controller; when the amplitude was −10 mm, it was 42.86% faster; and when the am-

plitude was −12 mm, it was 46.67% faster. Therefore, the designed fuzzy PID controller

had the stronger anti-interference ability.

5.2. Force Feedback Experiment

To verify the force feedback capability of the VISR in Section 2.3, a force feedback

experiment was conducted, as shown in Figure 20. The slave wire feeder of the VISR was

placed horizontally, the guidewire (ATW-595-ME014, Cordis Corporation, Dublin, USA)

was passed through the clamping component and extended forward to the front end of

the Y-type valve holder, and the sensor bracket was placed directly in front of the Y-type

valve holder. The experimental sensor (MDL: SBT674-2 kg, Simbatouch, Guangzhou, CN)

was connected to the sensor bracket by the spring, and the axis of the guidewire was kept

in a straight line with it. The operator propelled the master manipulator, and the slave

wire feeder drove the guidewire to propel the experimental sensor forward, which rec-

orded the force of the guidewire tip. At the same time, the resistance detection mechanism

measured the amplified force signal, and the control system adjusted the input excitation

current of the magnetic powder clutch according to the force, thus forming a force sensa-

tion. The feedback force could be converted according to the excitation current.

Spring Experimentalsensor

Resistance sensor

Guidewire

Slave delivery

Sensor bracket

(a) (b)

Figure 20. Force feedback experiment principle (a) and device (b).


The analysis showed that that the response speed of the fuzzy PID controller wasfaster. The conventional PID controller had a maximum amplitude of −18 mm and anaverage amplitude of 3.45 mm, while the fuzzy PID controller had a maximum amplitudeof −13 mm and an average amplitude of 2.37 mm. When the amplitude was −8 mm, theadjustment time of the fuzzy PID controller was 43.75% faster than that of the ordinary PID

Appl. Sci. 2021, 11, 611 14 of 19

controller; when the amplitude was−10 mm, it was 42.86% faster; and when the amplitudewas −12 mm, it was 46.67% faster. Therefore, the designed fuzzy PID controller had thestronger anti-interference ability.


To verify the force feedback capability of the VISR in Section 2.3, a force feedbackexperiment was conducted, as shown in Figure 20. The slave wire feeder of the VISR wasplaced horizontally, the guidewire (ATW-595-ME014, Cordis Corporation, Dublin, USA)was passed through the clamping component and extended forward to the front end of theY-type valve holder, and the sensor bracket was placed directly in front of the Y-type valveholder. The experimental sensor (MDL: SBT674-2 kg, Simbatouch, Guangzhou, CN) wasconnected to the sensor bracket by the spring, and the axis of the guidewire was kept in astraight line with it. The operator propelled the master manipulator, and the slave wirefeeder drove the guidewire to propel the experimental sensor forward, which recorded theforce of the guidewire tip. At the same time, the resistance detection mechanism measuredthe amplified force signal, and the control system adjusted the input excitation current ofthe magnetic powder clutch according to the force, thus forming a force sensation. Thefeedback force could be converted according to the excitation current.


Ideal curve

PID

Res

po

nse

(m

m)

Fuzzy PID

0

50

100

150

200

210

214

218

250

222

Time (s)

300

3 3.5 4

205

215

225

19519.5 20.5 21.5 22.5 23.5

2 5 8 101 3 4 6 7 9 110 14 17 20 2213 15 16 18 19 2112

4.5210

214

218

222

8.5 9 9.5 10 24.5

Amplitude: 10 Amplitude: 8 Amplitude: 12


The analysis showed that that the response speed of the fuzzy PID controller was

faster. The conventional PID controller had a maximum amplitude of −18 mm and an av-

erage amplitude of 3.45 mm, while the fuzzy PID controller had a maximum amplitude

of −13 mm and an average amplitude of 2.37 mm. When the amplitude was −8 mm, the

adjustment time of the fuzzy PID controller was 43.75% faster than that of the ordinary

PID controller; when the amplitude was −10 mm, it was 42.86% faster; and when the am-

plitude was −12 mm, it was 46.67% faster. Therefore, the designed fuzzy PID controller

had the stronger anti-interference ability.


To verify the force feedback capability of the VISR in Section 2.3, a force feedback

experiment was conducted, as shown in Figure 20. The slave wire feeder of the VISR was

placed horizontally, the guidewire (ATW-595-ME014, Cordis Corporation, Dublin, USA)

was passed through the clamping component and extended forward to the front end of

the Y-type valve holder, and the sensor bracket was placed directly in front of the Y-type

valve holder. The experimental sensor (MDL: SBT674-2 kg, Simbatouch, Guangzhou, CN)

was connected to the sensor bracket by the spring, and the axis of the guidewire was kept

in a straight line with it. The operator propelled the master manipulator, and the slave

wire feeder drove the guidewire to propel the experimental sensor forward, which rec-

orded the force of the guidewire tip. At the same time, the resistance detection mechanism

measured the amplified force signal, and the control system adjusted the input excitation

current of the magnetic powder clutch according to the force, thus forming a force sensa-

tion. The feedback force could be converted according to the excitation current.

Spring Experimentalsensor

Resistance sensor

Guidewire

Slave delivery

Sensor bracket

(a) (b)

Figure 20. Force feedback experiment principle (a) and device (b). Figure 20. Force feedback experiment principle (a) and device (b).

By comparing the actual force F1 of the guidewire, the measured force F2 of theresistance detection mechanism and the feedback force F3 of the master manipulator, thequality of the force feedback could be evaluated. In the experiment, the signal samplingperiod was −50 ms, and the experimental data after processing are shown in Figure 21.

It can be seen that, under the action of a time-varying force, the resistance detectionmechanism could accurately detect the actual force of the guidewire and reflect its changingtrend. When the guidewire was under force, the resistance detection mechanism couldamplify it with the amplification coefficient K1, which was determined to be two by themechanism design. According to the experimental data, the actual coefficient was between1.86 and 2.17 (K1 = F2/F1), and the error was less than 8.5%. The control system adjustedthe excitation current of the magnetic powder clutch according to the force signal to form aforce sensation. The amplification coefficient K2 could be determined through the designof the algorithm, which was set to 2.5. The actual coefficient was calculated from theexperimental data between 2.28 and 2.73 K2 = F3/F2, with an error of less than 9.2%.

Appl. Sci. 2021, 11, 611 15 of 19


By comparing the actual force 𝐹1 of the guidewire, the measured force 𝐹2 of the re-

sistance detection mechanism and the feedback force 𝐹3 of the master manipulator, the

quality of the force feedback could be evaluated. In the experiment, the signal sampling

period was −50 ms, and the experimental data after processing are shown in Figure 21.

Time (s)0 2 5 8 10 13 15

Fo

rce

(N

)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2Real Force

Measuring Force

Feedback force

1 3 4 6 7 9 11 12 14

Figure 21. Force feedback data.

It can be seen that, under the action of a time-varying force, the resistance detection

mechanism could accurately detect the actual force of the guidewire and reflect its chang-

ing trend. When the guidewire was under force, the resistance detection mechanism could

amplify it with the amplification coefficient 𝐾1, which was determined to be two by the

mechanism design. According to the experimental data, the actual coefficient was be-

tween 1.86 and 2.17 (𝐾1 = 𝐹2/𝐹1), and the error was less than 8.5%. The control system

adjusted the excitation current of the magnetic powder clutch according to the force signal

to form a force sensation. The amplification coefficient 𝐾2 could be determined through

the design of the algorithm, which was set to 2.5. The actual coefficient was calculated

from the experimental data between 2.28 and 2.73 𝐾2 = 𝐹3/𝐹2, with an error of less than

9.2%.

5.3. Clamping Force Experiment

The experiment was conducted to verify the flexible clamping performance of the

VISR in Sections 2.1 and 2.2, as shown in Figure 22. The operator repeatedly pinched and

released the film sensor in the operating handle to generate the clamping force signal. The

clamping component held the guidewire according to this, and the clamping sensor syn-

chronously recorded the actual clamping force of the guidewire. The operator varied the

force overtime and mimicked the trembles for when the doctor was tired. By comparing

the force signals measured by the film sensor and the clamping sensor, the performance

of the VISR in adjusting the clamping force could be judged. The experimental results are

shown in Figure 23.

Figure 21. Force feedback data.

5.3. Clamping Force Experiment

The experiment was conducted to verify the flexible clamping performance of theVISR in Sections 2.1 and 2.2, as shown in Figure 22. The operator repeatedly pinchedand released the film sensor in the operating handle to generate the clamping force signal.The clamping component held the guidewire according to this, and the clamping sensorsynchronously recorded the actual clamping force of the guidewire. The operator variedthe force overtime and mimicked the trembles for when the doctor was tired. By comparingthe force signals measured by the film sensor and the clamping sensor, the performance ofthe VISR in adjusting the clamping force could be judged. The experimental results areshown in Figure 23.


Film sensor

Operating handle

Clamping component

Clamping sensor

(a) (b)

Figure 22. Film sensor (a) in the operating handle and the clamping sensor (b) in the clamping component.

Time (s)

Fo

rce

(N

)

0

1

2

3

4

5

6

7

8

9Real Force

Measuring Force

2 5 8 101 3 4 6 7 9 110

10

Figure 23. Clamping force data.

According to the experimental data, the clamping force could be regulated according

to the force given by the operator at the master manipulator. When the operator pressed

the sensor, the clamping force quickly approached the peak, and when the operator re-

leased the finger, the clamping force rapidly decreased to zero, which indicated that VISR

was flexible enough for the clamping of the guidewire. Near the peak of the clamping

force, the accuracy rate of the first clamping was 85.4~92.7%, and the accuracy rates of the

last four clampings were above 95%. Therefore, it was believed that the actual clamping

force of the guidewire was close to the squeezing force given by human hands. When the

hand trembled, the VISR could eliminate it to some extent. There was a delay in the system

during operation, with the maximum not being more than −50 ms, which was within the

acceptable range.

5.4. Vascular Model Experiment

In order to verify whether the designed VISR could effectively assist the doctor in

surgery, an experiment was conducted to test whether the guidewire could be success-

fully propelled to a target position in a human vascular model. The model was designed

by the author under the guidance of doctors, and it could simulate the vascular environ-

ment of the human body to a certain extent. Ten operators were enrolled to operate the

VISR, as shown in Figure 24.


Appl. Sci. 2021, 11, 611 16 of 19


Film sensor

Operating handle

Clamping component

Clamping sensor

(a) (b)


Time (s)

Fo

rce

(N

)

0

1

2

3

4

5

6

7

8

9Real Force

Measuring Force

2 5 8 101 3 4 6 7 9 110

10


According to the experimental data, the clamping force could be regulated according

to the force given by the operator at the master manipulator. When the operator pressed

the sensor, the clamping force quickly approached the peak, and when the operator re-

leased the finger, the clamping force rapidly decreased to zero, which indicated that VISR

was flexible enough for the clamping of the guidewire. Near the peak of the clamping

force, the accuracy rate of the first clamping was 85.4~92.7%, and the accuracy rates of the

last four clampings were above 95%. Therefore, it was believed that the actual clamping

force of the guidewire was close to the squeezing force given by human hands. When the

hand trembled, the VISR could eliminate it to some extent. There was a delay in the system

during operation, with the maximum not being more than −50 ms, which was within the

acceptable range.


In order to verify whether the designed VISR could effectively assist the doctor in

surgery, an experiment was conducted to test whether the guidewire could be success-

fully propelled to a target position in a human vascular model. The model was designed

by the author under the guidance of doctors, and it could simulate the vascular environ-

ment of the human body to a certain extent. Ten operators were enrolled to operate the

VISR, as shown in Figure 24.


According to the experimental data, the clamping force could be regulated accordingto the force given by the operator at the master manipulator. When the operator pressed thesensor, the clamping force quickly approached the peak, and when the operator releasedthe finger, the clamping force rapidly decreased to zero, which indicated that VISR wasflexible enough for the clamping of the guidewire. Near the peak of the clamping force, theaccuracy rate of the first clamping was 85.4~92.7%, and the accuracy rates of the last fourclampings were above 95%. Therefore, it was believed that the actual clamping force ofthe guidewire was close to the squeezing force given by human hands. When the handtrembled, the VISR could eliminate it to some extent. There was a delay in the systemduring operation, with the maximum not being more than −50 ms, which was within theacceptable range.


In order to verify whether the designed VISR could effectively assist the doctor insurgery, an experiment was conducted to test whether the guidewire could be successfullypropelled to a target position in a human vascular model. The model was designed by theauthor under the guidance of doctors, and it could simulate the vascular environment ofthe human body to a certain extent. Ten operators were enrolled to operate the VISR, asshown in Figure 24.


Starting position

Target position

Guidewire position

(a) (b)

Figure 24. Human vascular model (a) and guidewire position (b).

In this experiment, each operator performed ten trials, and the guidewire positions

were recorded. The vascular model was measured to be 585 mm from the starting position

to the target position, and the actual transmission distance of the guidewire could be ob-

tained by the encoder in the slave wire feeder by taking the absolute value of their differ-

ence. The experimental results are shown in Figure 25.

Tester2 5 8 101 3 4 6 7 9

Po

siti

on

(m

m)

0

0.5

1

1.5

2

2.5

3Average

Max

Min

Figure 25. Guidewire position error.

As can be seen from Figure 25, the maximum position error of the ten operators in

this experiment was between 0.89–1.45 mm, the minimum error was less than 0.3 mm and

the average error of 8 operators was not more than 0.5 mm. In this experiment, the oper-

ators judged the position of the guidewire in their eyes, and since they were not profes-

sionally trained interventional physicians, the error was mainly caused by the operator

itself rather than the VISR. The designed VISR focused on the force feedback and flexible

clamping. Even if the amount of guidewire delivery was too large in the actual operation,

the force feedback mechanism could avoid the guidewire tip piercing the blood vessel

wall. Therefore, it is believed that the designed VISR can safely and effectively assist doc-

tors to deliver the guidewire accurately.

6. Conclusions

This paper introduces a novel vascular interventional surgical robot and its control

method, based on force feedback and flexible clamping. The robot is a master–slave sys-

tem, characterized by flexible clamping, real-time control of the clamping force and the

ability to accurately establish force feedback and assist doctors to make judgments on the

state of the guidewire tip. The motions of the robot include the clamping, twisting and

propulsion of the guidewire. In order to ensure the precision of the propulsive motion


In this experiment, each operator performed ten trials, and the guidewire positionswere recorded. The vascular model was measured to be 585 mm from the starting position

Appl. Sci. 2021, 11, 611 17 of 19

to the target position, and the actual transmission distance of the guidewire could beobtained by the encoder in the slave wire feeder by taking the absolute value of theirdifference. The experimental results are shown in Figure 25.


Starting position

Target position

Guidewire position

(a) (b)


In this experiment, each operator performed ten trials, and the guidewire positions

were recorded. The vascular model was measured to be 585 mm from the starting position

to the target position, and the actual transmission distance of the guidewire could be ob-

tained by the encoder in the slave wire feeder by taking the absolute value of their differ-

ence. The experimental results are shown in Figure 25.

Tester2 5 8 101 3 4 6 7 9

Po

siti

on

(m

m)

0

0.5

1

1.5

2

2.5

3Average

Max

Min


As can be seen from Figure 25, the maximum position error of the ten operators in

this experiment was between 0.89–1.45 mm, the minimum error was less than 0.3 mm and

the average error of 8 operators was not more than 0.5 mm. In this experiment, the oper-

ators judged the position of the guidewire in their eyes, and since they were not profes-

sionally trained interventional physicians, the error was mainly caused by the operator

itself rather than the VISR. The designed VISR focused on the force feedback and flexible

clamping. Even if the amount of guidewire delivery was too large in the actual operation,

the force feedback mechanism could avoid the guidewire tip piercing the blood vessel

wall. Therefore, it is believed that the designed VISR can safely and effectively assist doc-

tors to deliver the guidewire accurately.

6. Conclusions

This paper introduces a novel vascular interventional surgical robot and its control

method, based on force feedback and flexible clamping. The robot is a master–slave sys-

tem, characterized by flexible clamping, real-time control of the clamping force and the

ability to accurately establish force feedback and assist doctors to make judgments on the

state of the guidewire tip. The motions of the robot include the clamping, twisting and

propulsion of the guidewire. In order to ensure the precision of the propulsive motion


As can be seen from Figure 25, the maximum position error of the ten operators inthis experiment was between 0.89–1.45 mm, the minimum error was less than 0.3 mmand the average error of 8 operators was not more than 0.5 mm. In this experiment,the operators judged the position of the guidewire in their eyes, and since they werenot professionally trained interventional physicians, the error was mainly caused by theoperator itself rather than the VISR. The designed VISR focused on the force feedback andflexible clamping. Even if the amount of guidewire delivery was too large in the actualoperation, the force feedback mechanism could avoid the guidewire tip piercing the bloodvessel wall. Therefore, it is believed that the designed VISR can safely and effectively assistdoctors to deliver the guidewire accurately.

6. Conclusions

This paper introduces a novel vascular interventional surgical robot and its controlmethod, based on force feedback and flexible clamping. The robot is a master–slave system,characterized by flexible clamping, real-time control of the clamping force and the ability toaccurately establish force feedback and assist doctors to make judgments on the state of theguidewire tip. The motions of the robot include the clamping, twisting and propulsion ofthe guidewire. In order to ensure the precision of the propulsive motion and the acquisitionof a force signal, a two-dimensional fuzzy PID controller was designed, based on thedynamic model of the system, and a fuzzy control strategy was added. The performance ofthe VISR was verified by experiments. The experimental results showed that the designedfuzzy PID controller could improve the anti-interference ability of the system by more than40%. The VISR could accurately measure the proximal force of the guidewire and amplifyit, thus forming a force sensation for doctors. Among them, the measurement error wasless than 8.5%, and the amplification error was less than 9.2%. The guidewire could beclamped flexibly, and the clamping accuracy rate was about 95%. Therefore, the designedVISR could effectively assist doctors to deliver the guidewire accurately and safely.

However, there are several limitations in this paper. Firstly, the effect of the vascularenvironment on the force feedback mechanism was not considered in the establishmentof the force feedback mechanism. Second, the operator needs to be guided and trainedto perform the VISR. Finally, the lag in the master–slave motion needs to be reduced. Inthe future, we will improve the VISR to overcome the mentioned limitations and verify itsperformance through related experiments.

Author Contributions: Conceptualization, H.Y. and H.W.; methodology, J.C. and J.N.; software, F.W.and Y.Y.; formal analysis, H.T.; verification, J.F. and H.L.; writing—original draft preparation, H.Y.;

Appl. Sci. 2021, 11, 611 18 of 19

writing—review and editing, H.W. All authors have read and agreed to the published version of themanuscript.

Funding: This work was funded by the National Key Research and Development Program of China(2019YFB1311700), National Natural Science Foundation of China (U1713219) and Shanghai Scienceand Technology Innovation Action Plan (18441900700).

Informed Consent Statement: Not applicable.

Data Availability Statement: Data available in a publicly accessible repository.

Conflicts of Interest: The authors declare no conflict of interest.

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http://doi.org/10.1155/2013/536128

http://www.ncbi.nlm.nih.gov/pubmed/23997957

http://doi.org/10.1109/RBME.2012.2236311


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a novel vascular intervention surgical robot based on

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