Hardware in the Loop Simulation for Unmanned

Aerial Vehicles

NATIONAL

AEROSPACE

LABORATORIES

BANGALORE-560 017 INDIA

CSIR-NAL

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Shikha JainKamali C

Scientist, Flight Mechanics and Control DivisionNational Aerospace Laboratories

Bangalore, India

MATLAB EXPO 2016,4/21/2016

Introduction

Hardware-In-The Loop Simulation (HILS) is a real-time

simulation setup, in which the UAV platform is tested in the

same way as it is in the real experiment.

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The motivation is to develop a simulation and testing

framework that can be exhaustively used to examine the

performance of designed automatic flight control algorithms.

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Unmanned Aerial Vehicle

A mini UAV developed by NAL with surveillance as

the main application.

Table 1: A mini UAV geometric

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Parameter A (Military Version) B (Police Version)

Length 1.2 m 1.2 m

WingSpan 1.6 m 1.9 m

Weight 2 kg 2.5 kg

Payload <1kg <1kg

Table 1: A mini UAV geometric parameters

UAV designed by NAL

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Six DOF model for UAV

In order to develop a 6DOF model of the system, following data is required

Aerodynamic data

Propulsion data

Mass, Centre of gravity, inertia and moment reference point data

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Geometry data such as wing-span, mean aerodynamic chord and wing

surface area.

The 1:1 UAV model is subjected to the wind tunnel tests to yield the

aerodynamic coefficients.

The coefficients are in the look table form and it captures all nonlinearities.

Rigid body Equations of Motion are implemented in MATLAB/Simulink.

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Trimming & Linearization

Trimming

A nonlinear Least Squares (LS) minimization algorithm is

implemented to perform wings level trim.

Trim solution is used to start the simulation.

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Trim solution is used to start the simulation.

Linearization

Linearization is performed using central difference method.

The linear models are generated at trim point.

The linear models are used for control design.

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HILS framework

Host PC

Real Time Target Machine and Interfaces

A HILS framework contains four modules

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Real Time Target Machine and Interfaces

Autopilot Hardware

Ground Control Station

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HOST PC

The UAV aerodynamic model and engine model along with

equations of motions, sensor models, hardware interface blocks and

control algorithms are developed in Simulink platform.

Open loop, model in the loop, software in the loop and processor in

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Open loop, model in the loop, software in the loop and processor in

the loop simulation models are tested on Host PC.

For HILS

compiles the 6 DOF model and communicates it to the target machine

which is a Matlab real time kernel.

Runs the model in target machine.

Communicates with target machine through Ethernet.MATLAB EXPO 2016, 4/21/2016

6DOF Simulation model of UAVNATIONAL

AEROSPACE

LABORATORIES

BANGALORE-560 017 INDIA

CSIR-NAL

MATLAB EXPO 2016, 4/21/2016

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Control Algorithm

Using embedded real-time

target, autocode is

generated for the control

strategy and is burnt in the

micro controller.

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Real Time Target Machine and Interfaces

I7 Single Board Computer

Boots MATLAB real time kernel from CD.

Real time target machine is a real time operating system whichmeets the timing constraints required for real time applications.

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Acts as a root complex and Communicates with Spartan 6

FPGA through PCIe bus.

Spartan 6 FPGA

SPI, I2C, UART, USB, CAN, GPIO, PWMIO, ADC and DAC

IP run in Spartan 6 FPGA

The IP’s are developed in VHDL programming language

All hardware signals are isolated from Autopilot using

Digital IsolatorsMATLAB EXPO 2016, 4/21/2016

Autopilot Hardware

Focus on low weight with

reconfiguration capability.

Hardware is realized using

programmable systems on chip

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programmable systems on chip

(PSoC).

On board 3 axis accelerometers, 3

axis gyroscope, 3 axis

magnetometer, and, a static

pressure and temperature sensor.NAL Autopilot Version 3 (APV3)

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Ground Control station

Open source mission

planner software is used

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Used for direct

observation and

monitoring purposes.

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HILS Architecture

Classified based on the level of fidelity

Low fidelity

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Medium fidelity

High fidelity

Presentation covers the development of low

and medium fidelity HILS architecture

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Low fidelity HILS

Communication between target machine and autopilot hardware is

via serial communication.

Sensor data (IMU and GPS)are generated at regular interval.

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PWM signal are normalized and mapped to the flight control

parameters.

Low fidelity HILS architecture

Real Time

Target Machine

Autopilot board

Serial

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Detail connection diagram15

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Communication protocol

Real Time

Target Machine

Autopilot board

Protocol

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Data Length

PayloadCheck Sum

1 byte 1 byte Data Length 1 byte 1 byte

Total Count= Data length(byte)+ 4 bytes

Check Sum Range

Protocol

Flow of code in the Target Machine

Receive Data Packet Scaling of Data

Decode the Packet

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Mapping of Data to Flight Model

Formation of packet

Decode the Packet

Normalization of Data

Send packet through UART/USB

6 DOF Simulation

Model

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Flow of code in an autopilot board

Generate Control

Execute Control Subroutine

Mapping of sensor data to

the control subroutine

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Receive Data Packet via

UART

Generate Control Deflections (PWM)

Decode & scaling of Data Formation of

Packet

Send Packet to Target Machine through UART

Send packet to Ground Control

Station for Visualization

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Snapshot

Target Machine

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Host PC Visualization PC

NAL Autopliot

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Medium fidelity

Sensors data is sent over

the interface supported by

the autopilot hardware

such as I2C, UART.

Real Time

Target Machine

Autopilot board

Sensor data

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such as I2C, UART.

Feedback to the flight

model in target machine is

obtained from the actuator

(servos).

Medium fidelity HILS architecture

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Actuator(Servos)

Block Diagram22

Flow of code in the Target Machine

Receiving Analog

Voltages

Scaling of sensor data

Framing of Aircraft

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Mapping Voltage values to the Flight

control parameters

Framing of packet

Sending data:IMU through I2C interface

GPS through UART interface

Aircraft6DOF

Simulation Model

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Flow of code in an autopilot board

Receive Data Packet

via (I2C & UART)

Packet Decode &

scaling of Data

Mapping of sensor data to

the control subroutine

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Generate Control

Deflections (PWM)

Drive Servo Motors

Execute Control

Subroutine

Encode packet for visualization in Ground Control

Station

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Snapshot

Visualization PC

Target Machine

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Host PCVisualization PC

NAL Autopliot

Servos

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Results

UAV performing way point navigation

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Conclusions

Design and development of a low and medium

fidelity HILS for a mini UAV is presented.

The development is based on Model Based Design.

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The development is based on Model Based Design.

These architectures are used to verify the working

of control code and sensor fusion algorithms on

the autopilot board.

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Acknowledgements

Our sincere thanks to

Director, NAL

Dr. G. K Singh and his team

Dr. C M Ananda and his team

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Dr. C M Ananda and his team

Dr Jatinder Singh, Head FMCD

Dr Abhay A Pashilkar, Group head, Flight Simulation

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References

1. C Kamali, Alexander Kale,” Development of Six DOF model for Class-I MAV”, September, 2013.

2. “Preliminary test results on 1:1 scale UAV models” Report No. HAL/ARDC/UAV/WNT/001, July 2012.

3. Alexander Kale, Shikha Jain, C Kamali,” Simulation, Modal Analysis and Parameter Estimation of Class-I MAV”, October

2013.

4. Shikha Jain, C Kamali,” Experimental Validation of NAL’s Class 1 MAV Simulation Model Using Flight Data”, December

30

2013.

5. Software In the Loop Simulation (SILS) for NAL’s Class 1/Similar class MAV.

6. Arya, Hemendra,(2010), Hardware-In-Loop Simulator for Mini Aerial Vehicle, Centre for Aerospace Systems Design and

Engineering, Department of Aerospace Engineering, IIT Bombay, India.

7. Dongwon Jung and Panagiotis Tsiotras (2007), Modelling and Hardware-in-the-Loop Simulation for a Small Unmanned

Aerial Vehicle, AIAA Infotech at Aerospace, Rohnert Park, CA.

8. A. Gholkar, A. Isaacs, and H. Arya, (2004), .Hardware-In-Loop Simulator for Mini Aerial Vehicle, Sixth RealTime Linux

Workshop, Nanyang Technological University (NTU), Singapore.

9. Viswanathan S, Guruganesh R, “Design of Autopilot for Class I MAV using Classical Control”, November 2013.

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Thank You31

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