OPEN DESIGN FOR HELICOPTER ACTIVE CONTROL SYSTEMS

Geoffrey J. Jeram School of Aerospace Engineering Georgia Institute of Technology

Atlanta, Georgia

Abstract

With a holistic approach, this open design for helicopter active control systems applies intelligent control techniques to provide the pilot with useful and intuitive tactile cues. The design has five interdependent functional modules that provide: limit prediction and avoidance cues based on neural networks; heuristic cues based in fuzzy inference systems; an intelligent arbitrator to distribute multiple and possibly conflicting cues for multiple control axes; and a tactile interface to define appropriate force characteristics for various kinds of cues. Each module is explained as a process in the context of practical applications that include maneuver envelope limit avoidance cues, emergency procedure prompts, instrument cues, robot assistance for routine tasks, and system customization to the pilot. The design is demonstrated as a limit avoidance cueing system in the Real-Time Interactive Prototype Technology Integration Development Environment (RIPTIDE) at the Army/NASA Rotorcraft Division.

Nomenclature

f , g , h = Vector functions

F = Vector of active control axes counter forces from the active inceptor to the pilot, F = [ Fcol Flong Flat Fpedl ]T

u = Control vector of k elements u = [ δ1 δ2 …δk ]T

x = Aircraft State Vector of n elements x = [ x1 x2 x3 … xn ]T

y = Aircraft Limit Vector of m elements y = [ y1 y2 y3 … ym ]T

yp = Predicted Limit Vector

∆ucrit = Control margin vector

∆y = Limit margin vector

Subscripts

ANN = Adaptive Neural Network

coll = Collective

long = Longitudinal (cyclic)

lat = Lateral (cyclic)

crit = Critical

fut = Future

p = Predicted

NN = Neural Network (implies static)

* = Assumed (chosen) future time history (as in u*)

Introduction

Background and Context

The tactile connection between man and machine is a physically direct connection. The aerodynamic forces on the aircraft

control surfaces travel through rods and cables to the pilot’s arms and legs. These control forces inform the pilot about the aircraft

through his muscles and joints. It is a proprioceptive perception, that is, a sense originating within the organism, and it is

distinguished from visual and vestibular perception. The pilot’s use of this proprioceptive information is commonly referred to as

“seat of the pants” flying. As aircraft grow larger and incorporate hydraulics, power assist servos, and fly-by-wire technology, their

control systems sever the physical connection between the pilot’s cockpit controls and the aerodynamic forces. The pilot loses a

channel of information and it is more difficult for him to “get the feel for the aircraft” and use it a natural extension of his body.

Active control inceptors are cockpit controls such as collective levers, sidesticks, yokes, and cyclic sticks that generate artificial

forces against the pilot’s hands and feet. With active inceptors, a control system can use forces to provide the pilot with

information about the aircraft. Advanced aircraft with highly unconventional configurations operating across several regimes of

flight and with highly non-linear control dynamics do not react the same way the conventional airplanes did. But with intelligent

limit prediction systems, artificial force cues can return to the pilot a somatic understanding of the aircraft. Moreover, an active

inceptor can physically manipulate cockpit controls. An intelligent active control system can serve the pilot as a robot assistant that

performs routine tasks, prompts emergency procedures, and otherwise lightens pilot workload.

Aircraft limit prediction systems linked to tactile cues in flight controls enable more carefree handling and help us make the

most of an aircraft's flight envelope1. Recent studies such as the Helicopter Maneuver Envelope Enhancement (HELMEE)

program have shown as much in the NASA Ames' Vertical Motion Simulator2. Other ongoing projects such as the Helicopter

Active Control Technology program sponsored by the U.S. Army and carried out by Boeing continue to explore the potential of

active cueing3. The Army/NASA Rotorcraft Division continues to develop its Rotorcraft Aircrew Systems Concepts Airborne

Laboratory (RASCAL) in a JUH-60 Blackhawk airframe4. With a two-axis active sidestick controller and a full-authority fly-by-wire

flight control system, the RASCAL facilitates active control and limit cueing research. The Army/NASA Rotorcraft Division

Flight Mechanics and Cockpit Integration Branch and the School of Aerospace Engineering at the Georgia Institute of Technology

are developing the control system for the RASCAL active sidestick. One product of the endeavor is this holistic approach and

open design architecture. It is intended for the RASCAL active control system and is applicable to general haptic applications.

Motivation and Significance

The active control projects of the last decade, including many limit avoidance and tactile cueing projects, have advanced the

theory of limit prediction and tactile cueing for avoidance. When implemented in simulation and practical evaluation, the active

control systems have been specific designs for targeted applications, as in the HELMEE project, and or are tightly integrated into

the aircraft flight control system, as in the Helicopter Active Control Technology (HACT) program5. This project likewise began

as a limit avoidance system targeted for the RASCAL Helicopter, but it took a holistic approach and the design has broadened into

an open engineering system. It has an architecture that maintains design freedom, flexibility, and growth potential as far as

possible. As limit prediction models are refined and new ones devised, they can be fitted into this architecture, replacing previous

algorithms or adding to their capability and accuracy. It is intended for diverse applications in the aerospace industry and beyond.

Several specific new concepts and techniques are developed within this overall design. Previous limit avoidance cueing

methods successfully mapped a single limit to a single control axis. But these programs treated the limit-to-control relationship as

one-to-one. The limit surface search algorithm created with this design uses the non-linear mapping of the limit surface to search

across admissible control positions to find the critical position that will cause the aircraft to exceed a limit. Unlike previous

methods, it accounts for highly non-linear, not one-to-one limit surfaces. It allows for the possibility that the same limit might be

reached on the same control axis in either direction. Another specific advance is a method for transient limit predictions. This

design includes novel prediction and cueing methods for transient limits such as peak flapping.

The most significant advance offered with this design is the use of logically based cues. Previous active control system

advances are confined to classical control methods used primarily for limit avoidance cues and maneuver envelope enhancement.

Only the HACT program is developing more sophisticated guidance cues for station keeping, energy management, and flight path

cues6. This design offers similar guidance and robotic cues using intelligent control systems, specifically fuzzy inference systems for

situation identification and fuzzy logic controllers for guidance. The logic-based cues do not rely on crisp mathematical

relationships as arithmetically based cues do. These methods complement the tactile cuing and active control concepts advanced in

the Helicopter Active Control Technology (HACT) program. These fuzzy reference systems also play a key role dealing with

combinations of cues.

A final significant aspect of this design is its early development in RIPTIDE. This is one of the first applications of

RIPTIDE for novel research and is the first active control program to do so. RIPTIDE provided the results presented here.

Cueing Concepts and Terms

This design recognizes arithmetically based cues and logically based cues. Arithmetically based cues depend on numerical

values. They are calculated from dynamical and probabilistic models and the neural network is the primary tool for these methods.

Limit avoidance cues are examples of arithmetically based cues. Logically based cues depend on heuristics. They are inferred from

cause and effect relationships, conditional equations, and possibilities. Fuzzy logic is the primary tool for these methods.

Procedural cues are examples of logically based cues.

Arithmetically Based Cues

Arithmetic cues rely on a state space dynamical aircraft model to represent the system of aircraft states, inputs, and outputs.

( )uxy ,h=( )uxx ,g=& ( 1 ) ( 2 )

The state vector, x, is a defining set of aircraft motion characteristics and the input, u, is the vector of physical displacements of

the cockpit controls. With information about the states and the controls, and an accurate model of the dynamic interaction

between them, the mathematical solution provides the future state of the aircraft. The limited parameters (or limit vector), y, is a

vector of individual limits, yi , each of which is an algebraic function of the present states and inputs. Often, a limit parameter is

identical to the value of a state.

Depending on the context, the word limit may refer either to the name of the limited parameter (such as Vertical Load or

Airspeed) or to a critical value of that parameter (such as 4 G’s or 150 Knots). The future limit margin is defined as the difference

between the limited parameter critical value and the value of that parameter at some future time.

futlimfut iii yyy −=∆ ( 3 )

A control, also called an inceptor, is the physical lever that is the interface between the pilot’s applied forces and displacements

and the Flight Control System’s information medium. The control margin is defined as the difference between the present control

position and the critical control position where, if the pilot displaced the controls to that position, the aircraft would reach the critical

limit value, the limit. A limit may be a function of the control configuration and flight condition, ylim(x,u), but usually it is a constant

maximum or minimum allowable value. A limit has a corresponding upper control margin, when there exists a critical control position

greater than the present control position. Likewise, a limit has a lower control margin, when there exists a corresponding critical control

position less than the present control position.

( 4 ) ocrit uuu −=∆The relationship between the future limit margin and the present control margin is non-causal, non-linear, and non-bijective. To

establish a causal relationship and enable practical limit avoidance cueing, every limit prediction model makes a future transition

assumption for each limit. With this assumption, the present aircraft state, xo, and the control position, uo, a limit prediction model

provides a predicted limit vector, yp(xo,uo) , or predicted limit, yip . The predicted limit margin is defined as the difference between the

predicted limit and the critical limit value or limit.

( 5 )

In a limit avoidance cue, the cueing system

approximates a mapping between the predicted limit margin

the present control margin. This mapping of a predicted

limit to the critical control position is the essence of

effective limit avoidance tactile cueing (Figure 1).

and

LimitMarginPresent

MAX

Control Lever

ControlMargin

Map to

( )plim ii yy − ( )ocritical δδ −

Critical Control Position

LimitMarginPresent

MAX

Control Lever

ControlMargin

Map to

( )plim ii yy − ( )ocritical δδ −

Critical Control Position

Figure 1. The Key to Effective Tactile Cueing

plimp iii yyy −=∆

Logically Based Cues

In this design, logical cues rely on fuzzy logic inference systems. The aircraft states, controls, and limits are the fuzzy variables

for the inference system. For example, airspeed as a fuzzy variable is not operated on as a numerical value of 60 knots. Instead it is

described by membership function such as “cruise speed”, “hover”, or “below ETL”. Likewise, an output such as collective position,

has fuzzy membership functions such as “forward”, centered”, or “aft”. Each membership function is a unimodal possibility

distribution across a universe of discourse, analogous to a function domain.

A fuzzy inference system follows five steps. First, it fuzzifies the input, converting it from a numerical value into a

membership function. Second, it applies the fuzzy operators analogous to the logical AND, OR, and NOT. Third, it applies an

implication method. This is a rule described as an IF – THEN relationship. Fourth, the results for all the rules are considered

simultaneously and aggregated. Finally, aggregate result is defuzzified to number. The rules are defined from expert knowledge

such as pilot experience, aircraft technical manuals and handbooks, and aviation textbooks. For example, the rules for an

emergency procedure cue are a pilot’s answers to: “What are the indications that make you realize and identify an emergency

condition?” and “What to you do to remedy the emergency?” These become IF-THEN relationships that infer the logical cue.

Design Approach and Architecture

The Holistic Control System Approach

In this design, the active control system is a self-contained and independent control system. It is not a subsystem of the

aircraft Flight Control System or even a system strictly in series with the Flight Control System. Rather, it is nested or echeloned

within the overall closed loop. (Figure 2). This holistic approach allows pre-existing flight control systems (FCS) and if the active

control system is adaptive, it can function properly for different aircraft dynamics and flight control systems.

Aircraft control system designs commonly model the pilot as a subsystem in a closed loop flight control system model. With

the addition of an active inceptor, like an active sidestick, the closed system effectively has another feedback loop internal to the

pilot subsystem. That feedback loop is the force-counterforce interaction between the pilot and the active sidestick. That feedback

channel does not affect the flight control system except by changing the nature of the resulting stick displacement as though the

aircraft had a more experienced and knowledgeable pilot. But the active control system still uses input from the aircraft state just as

the FCS does. The active control system can also use additional information about the aircraft limits, emergency procedures and

other sources that the FCS does not attend to. Tactile cueing can signal the pilot of an impending limit and guide his actions

without over-riding them as the FCS would. The pilot retains full authority over the controls.

FlightControl System

AircraftDynamics

ControlSurfaces

Feedback to theFlight Control System

PerceptionVisualVestibularProprioceptive

Flight Control System(Digital and Electronic)

PerceptionVisualVestibularProprioceptive

The Pilot

Action“Hands On”

ControlForces

Active InceptorForce – Displacement

Algorithm

Force

Counter Force

Feedback to theActive control

systemFeedback to the

Pilot

Pilot-Inceptor system in the physical world

InceptorDisplacement

Figure 2. Holistic Approach to the Design

Modular Architecture

This active control design is treated as

an open engineering system and strives to

incorporate robustness through adaptable

networks and other methods, and

modularity through five functional modules

(Figure 3). Each module is set of

subsystems with one of five functions: Limit

Prediction; Critical Control Position

Calculation; Logical Cues; Intelligent

Arbitration Among Cues; and Tactile

Method Interface. Each of these functional

modules is explained in detail.

The modular architecture facilitates

the use of proven successful systems, such

as limit prediction schemes. It also allows

additions of new or improved systems and

growth into an increasingly comprehensive

assistant for the pilot.

Limit Prediction Module

Each limit parameter has at least one

prediction system in this module. Each is defined by three characteristics: the limit, the method of prediction, and the type of

prediction. Whether a limit is derived from structural failure criteria, flight control system domain boundaries or regulatory

requirements, these arithmetic based systems require a numerically defined limit that depends on aircraft states and controls.

Relevant LimitExamples: Vertical Loading Forward & Lateral AirspeedTorque Angle of AttackFlapping Blade Stall

Limit prediction methodMath Models

Static Neural NetworksAdaptive Neural Networks

Type of predictionDynamic Trim

Fixed Time Horizon

Limit Prediction - Arithmetic Cues

Local Sensitivity Methods:Limit-to-Control Partial DerivativePseudo-Inverse of Limit GradientWeighted Inverse of Limit Gradient

Predicted Limit Surface Search

Critical Control Calculation

Emergency CuesRoutine CuesInstrument CuesPilot Customization

Logical Cues

Most Conservative CuesIntelligent selection among conflicting cues ( Rule-Based de-confliction and priority )Relative weighting of controls may vary with limit sensitivity and flight conditions.

Intelligent Cue Arbitrator

Tactile InterfaceForce inversely proportional to control marginStep force at critical control positionShaking (choose magnitude and frequency)Friction Damping Detents Inverse-detents

Relevant LimitExamples: Vertical Loading Forward & Lateral AirspeedTorque Angle of AttackFlapping Blade Stall

Limit prediction methodMath Models

Static Neural NetworksAdaptive Neural Networks

Type of predictionDynamic Trim

Fixed Time Horizon

Limit Prediction - Arithmetic Cues

Local Sensitivity Methods:Limit-to-Control Partial DerivativePseudo-Inverse of Limit GradientWeighted Inverse of Limit Gradient

Predicted Limit Surface Search

Critical Control Calculation

Emergency CuesRoutine CuesInstrument CuesPilot Customization

Logical Cues

Most Conservative CuesIntelligent selection among conflicting cues ( Rule-Based de-confliction and priority )Relative weighting of controls may vary with limit sensitivity and flight conditions.

Intelligent Cue Arbitrator

Tactile InterfaceForce inversely proportional to control marginStep force at critical control positionShaking (choose magnitude and frequency)Friction Damping Detents Inverse-detents

Figure 3. Modular Design Architecture

Relevant Limits

This design loosely classifies aircraft limits into three categories. The category of limits very highly sensitive to control surface

movements are appropriately avoided by the aircraft flight control system without cueing to or input from the pilot. A joint

U.S./France study6 of helicopter limits for cueing identified 39 limits that fall into the remaining two categories. These limits are

most effectively cued through combinations of visual warning lights and instruments, aural warning and caution tones, verbal

(voice) warnings, and tactile cueing through the cockpit controls. The second category, which includes limits that vary slowly over

time such as transient limits on the order of seconds, are appropriately cued by non-tactile means. The third category includes the

limits that are sensitive to inceptor displacement within a few tenths of a second or are sensitive to inceptor speed within a few

hundredths of a second. This module predicts limits for these two categories, primarily the latter. Examples for such limits

include: vertical load, main rotor blade stall, main rotor flapping, main rotor speed, and transmission torque.

Type of Prediction

Two limit prediction methods make two distinctly different types of predictions. The difference is in their assumptions about

the aircraft’s transition from the present to the future. The fixed time horizon prediction calculates the value of the limit parameter

at a fixed distance in the future. In this case, the future transition assumption is an assumed future time history for the controls.

The dynamic trim prediction, calculates the limit parameter value for the aircraft dynamical system (equations 1 and 2) in a quasi-

steady equilibrium. The future transition assumption in this case, is an assumed transition for the states.

Fixed Time Horizon (FTH)

This type of prediction assumes that the controls will follow some defined path to a chosen point in the future. The fixed

time horizon prediction may assume the controls follow the worst-case path. More commonly, the controls are assumed to follow

a path similar to the path followed by the pilot during actual or simulated test flights that provide time history data. The fixed time

horizon method maps the relationship between the vector of states and controls at each time, to, to the limit value at time, to + ∆t.

This mapping can be captured in any number of ways, most effectively in neural networks as described below. The advantage of

this method is that the time frame for the prediction is known and, depending on the limit, can be reasonably accurate to as far as a

half second into the future. ( )( )

( )( ) KK

∆+∆+

tttt

tt

o

o

o

o

ux

ux

h f

( ) ( ) ( ) KKK ttttt ooo ∆+∆+ 2yyy

Kh f

( )( )

( )( ) KK

∆+∆+

tttt

tt

o

o

o

o

ux

ux

h f

( ) ( ) ( ) KKK ttttt ooo ∆+∆+ 2yyy

Kh f ( )uxfy ,=FTHp ( 6 )

Typical time horizons (∆t) are 0.25 to 0.46 seconds7. These prediction times are far enough to give the pilot time to react, but

not so far that the prediction loses accuracy. More distant time horizons loose accuracy due to pilot self-determination. That is,

the pilot is likely to choose a future control path unlike control path of the aggregate training data for the prediction model.

Dynamic Trim (DT)

The dynamic trim prediction8 separates the n aircraft states into k “slow” states that vary slowly with time, and (n-k) “fast”

states that vary quickly and reach a steady value during a maneuver.

n

fast

slow R∈

=

xx

xkn

fast R −∈x kslow R∈x ( 7 )

The future transition assumption is that the controls and the predicted slow states do not change while the fast states have

changed and settled to a constant. The predicted limit follows from the solution to the dynamical system (1) and (2) in the form:

( ) ( )uxhyuxgx

,,,0

==

DTp

slow&( 8 ) ( 9 )

The manner in which the fast states transition to steady and the time they take is irrelevant to the method. Consequently, the

prediction time is not defined. In practice, the dynamic trim solution can be difficult to find for complex dynamical models, but an

adaptive technique9 can approximate the dynamic trim prediction model from time history a posteriori.

The dynamic trim prediction is useful for aircraft in forward flight or in any flight profile where fast and slow states can be

discerned. It gives good predictions for the worse case limit values possible during a maneuver. While the prediction time horizon

is undefined, this characteristic is generally evident from inspection of the time history of the prediction and the actual limit value.

Limit Prediction Methods

Math Model

This prediction method uses a model for predicted limit, yp, derived from a priori understanding of the aircraft dynamics.

( )uxy ,fp = ( 10 )

This method solves the state equation (1) based on the future transition assumption. For the dynamic trim prediction, the

assumption defines values for the fast states. For the fixed time horizon prediction, the assumption defines the control future time

history. The one special form of the math model that requires no future transition assumption is the zero time horizon prediction,

which is not a prediction at all. In that case, the present limit is used as the prediction, yp=y. The math model produces a virtual

table of limit predictions for given states and control values. This can take the form of an actual look-up table for use with multiple

argument interpolation, but more commonly this prediction method is a preliminary step to create neural network training data.

Static Neural Networks

An artificial neural network is a mathematical construct, such as a polynomial or a combination of vector functions called

basis functions (such as the sigmoid, tan-sigmoid, and radial basis functions). Based on error back-propagation, this construct has

parameters that self-adjust to provide a target output. Neural networks capture the a posteriori relationship between the controls and

the predicted limits based on representative pattern and target data. Math model solution sets can provide this data directly or the

time history data from flights and simulations can provide it. Static network training is completed with all the data available at once.

Type Training Patterns Training Targets

Dynamic Trim xslow(t) , u(t) fNN(x,u) yDT(t)

Fixed Time Horizon x(t) , u(t) fNN(x,u) y(t+∆t)

( ) ( ) ( )( )ttt NNp uxfy ,= ( 11 )

Prediction error is a common practical difficulty with math model and static neural network predictions because aircraft

parameters and flight conditions change, as when the center of gravity shifts or pilot control characteristics change. The HELMEE

and HACT projects correct prediction errors using complementary filters that effectively eliminate steady state prediction errors.

But while this technique performs an essential function, the filters cloud the output from the prediction model.

Adaptive Neural Networks

Adaptive neural networks offer an alternative method to correct real time prediction errors and, unlike filters, they improve

the prediction function to capture local or transient variations in the dynamical relationship of states, limits, and controls. Unlike a

static network, an adaptive network adjusts the neural network weights incrementally, as additional pattern and target pairs are

presented. In other words, the adaptive neural network uses time history data in real time to reduce the prediction error and

improve the prediction model. In order to use an

adaptive network to approximate the predicted limit, it

must have a measured or inferred value for the limit

parameter to use as its real time target.

Alternatively, the adaptive net could model local

state dynamics as shown in Figure 4, instead of the limit

parameter. In this method, the limit parameter is

modeled as a dynamic system using a mathematical

model or a static neural network trained with

representative time history data. The Approximate

Model captures the dynamic complexity (high order

characteristics) of the limit parameter. The Approximate

Model should be a good global representation of the

parameter dynamics. Given a powerful enough control

computer, the Approximate Model could be a

Adaptive Net

K

Helicopter DynamicsHelicopter Dynamics

+

x & ˆ

x & ˆ ∆

∫ x′&

x

( )ux , ANN f

ApproximationMath Model

or Static Neural Net

( )uxx ,ˆ NNf=&

),(ˆ uxx g=&u

e+

+

+-

xu

( ) u x , ˆ h yp =

( )

Approx. Model

Adaptive Net

= ∆ t +

Forward Euler Method

Nx+

xx =0ˆ

*u

1Nˆ +x

y∆lim y

Figure 4. Adaptive Neural Net Application with a Transient Limit

comprehensive math model such as GenHel. The Adaptive Neural Net (ANN) supplements the approximate model with a

reduced order correction to correct for local and short-term variations in the parameter dynamics. The ANN is kept simple for

two reasons: 1) to reduce computational demands for real time adaptation and 2) to prevent the ANN from dominating global

dynamics with local and short term dynamics. For fast transient limits, this dynamical model must be accurate for short time

predictions, on the order of 0.05 seconds, but the model need not be accurate beyond that time horizon.

With a dynamical model for the transient limit parameter (made from the combination of the approximate model and the

adaptive neural net) this prediction scheme uses a forward Euler approximation to model the future response of the parameter.

The system tracks the maximum and minimum values for the modeled parameter to find the peak. Here, the future transition

assumption exists in the choice of the future time history of the control, u*. A reasonable assumption for u* could be the fastest

and farthest the pilot could realistically move the control within the time horizon. This prediction algorithm could be repeated with

several variations for u* and the worst likely result chosen for the prediction.

The time frame for fast transient limits is too brief for effective pilot cueing with a force that depends on control position (a

“softstop”). Transient limits like main rotor flapping with respect to cyclic and vertical loading with respect to collective are more

strongly influenced by the speed of control movement rather than control position. A more effective cueing method alters the

inceptor damping. The limit margin, ∆y, is defined as the difference between the maximum allowable transient peak and the

predicted peak for the limit parameter. The damping cue is proportional to the worst-case fast transient limit margin.

Critical Control Position Module

Local Sensitivity Methods

Each system in this functional module establishes a relationship between a limit and the controls. Local sensitivity methods

depend on the limit gradient or the limit vector Jacobian, also called the limit sensitivity matrix. This method approximates a linear

limit-to-control relationship using the tangent to the limit surface defined by the math model, yp=f(x,u), or neural network

yp=fNN(x,u). If the limit prediction function is well understood, the predicted limit Jacobian can be found analytically. If not, the

local limit sensitivity is found through perturbation methods, iterating on its limit prediction system as a subsystem or subroutine.

For the non-predictive limit models, yp=y, the critical control position equals the current control position, ucrit=uo

These methods have the advantage of computational speed. The disadvantages are those inherent in the linearization. The

limit surface may be highly nonlinear and local sensitivity values may vary considerably with small changes in the state or control.

Also, it is not uncommon for the current control position on the limit surface to lie at a local minimum or maximum where the

same limit is reached whether a control is moved one direction or the opposite. Linearization will fail to predict accurate critical

control positions for these conditions.

-100% 100%Control Position u2

Con

trol P

ositi

on

u1

100%

-100%

CurrentControlPosition

Limit Parameter vs. Two Active Control Axes

LocalLimit

Surface

1u∆

2u∆

-100% 100%Control Position u2

Con

trol P

ositi

on

u1

100%

-100%

CurrentControlPosition

Limit Parameter vs. Two Active Control Axes

LocalLimit

Surface

1u∆

2u∆

Figure 5. Critical Position from Limit Partial Derivative

Limit-to-Control Partial Derivative

This simple method finds the control margin by dividing the

limit margin by the limit sensitivity for each control axis (Figure 5).

( 12 ) ( )ii p

j

ij yy

ufu −

∂∂

=∆−

lim

1

This limit sensitivity method estimates the critical position for each

active axis independently. The HELMEE study used this method

effectively to cue each limit along a distinct active control axis.

Critical PositionCritical Position

Pseudo-Inverse of Limit Gradient

An alternative method8 treats the controls together as a vector

and uses the Jacobian’s pseudo-inverse to find the control margin

vector to the “nearest” control combination that zeros the limit

margin. This nearest distance is the least-squared distance of each

axis control margin. This method weights each of all the controls

equally.

( 13 )

The critical control position for each axis follows directly from

the control margin vector decomposition. This method works fairly

well when one limit is moderately influenced by two or more active control axes.

Control Position u2

Con

trol P

ositi

on

u1

100%

-100%

CurrentControlPosition

Limit Parameter vs. Two Active Control Axes

LocalLimit

Surface

u∆

Control Position u2

Con

trol P

ositi

on

u1

100%

-100%

CurrentControlPosition

Limit Parameter vs. Two Active Control Axes

LocalLimit

Surface

u∆

Figure 6. Pseudo-Inverse of Limit Gradient .

( )ii p

i yyf−

∂∂

=∆+

limuu Critical PositionCritical Position

Weighted-Inverse of Limit Gradient

A variation of the previous method multiplies the pseudo-inverse by a weight matrix. This weight vector may be a function

of the states to emphasize or de-emphasize control axes at different flight conditions

( 14 )

Predicted Limit Surface Search

A new method developed for this active control system

design uses a limit surface search algorithm to find the critical

control position. This method begins a search at the current

control position, xo, and samples the prediction models,

yp(xo,u), at increasing and decreasing positions for each of the

active control axes in turn. Throughout the search the present

(instantaneous) state vector is used. When the resulting

prediction for a limit first moves into its set of prohibited

values, that control position becomes the critical control

position. A prohibited value for the limit parameter is one

beyond the maximum or minimum allowable or may be a

dangerous or damaging internal subset of values.

This method does not assume a positive or negative

relationship between the control and the limit. It does allow

the possibility that the non-linear inverse may not be one-to-

one. It has these advantages over the local sensitivity methods described earlier. Its chief drawback is its computational demand.

Without a capable active control system computer, the designers may need to simplify the complexity of the neural network used

for the limit prediction or reduce the resolution of the limit surface search. The latter option is usually best since the prediction is

itself only an approximation and there is no need to search to high precision a limit surface of lower precision.

kR∈u kkn RR ×→:W( ) ( )ii p

i yyf−

∂∂

=∆+

limuxWu nR∈x

Current Control PositionCurrent Nz ( δcoll , δlong , y )Predicted Nz value ( δcoll , δlong , yp )

Current Control PositionCurrent Nz ( δcoll , δlong , y )Predicted Nz value ( δcoll , δlong , yp )

Figure 7. Predicted Limit Surface Search Algorithm.

The Mesh Surface of Figure 7 represents a predicted limit (vertical loading, Nz) with respect to collective and longitudinal

control axes. At the depicted instant in time, during a pull up maneuver, when the control and limit coordinate is positioned at

(δcoll, δlong ,y), the search algorithm begins at the predicted limit for the current control position (δcoll,δlong ,yp ). The algorithm

varies each control position in the prediction function away from the start position, along the admissible control positions shown as

black lines. When the prediction exceeds the limit (in this example ylim+= Nz(max)=1.5), that control position is defined as the critical

control positions for each axes for that instant. Those critical upper critical control positions are indicated in red and blue lines.

Note in this example that the predicted limit decreases at very high collective positions. Had the limit been set a little higher (i.e.

Nz(max)=1.6), the algorithm would not find a critical position for collective because no position along the collective search path

exceeds the limit.

Logical Cues Module

These logically based cues do not rely on numerically defined limits and arithmetically determined cues. Instead, fuzzy logic

inference systems serve as expert controllers and the active control system effectively becomes a robot assistant. These cues assist

the pilot with routine tasks, prompt him to initiate emergency procedures, assist with instrument flight, and customize the active

controls to individual pilots. In many cases, these cues are translated to force characteristics qualitatively different from limit

avoidance cues and do not interfere with them. In other cases, the arbitrator must prioritize them among limit avoidance cues.

Emergency Procedure Prompts

Emergency prompt systems integrate two functions: emergency situation identification and tactile prompting for immediate

action. Fuzzy logic heuristics detect, identify, and verify the emergency condition using indications delineated by the aircraft

operator’s manual emergency procedures section and other expert sources. These heuristics also consider the pilot’s action or

inaction and assess the criticality and appropriateness for an emergency prompt. When an emergency condition is verified and

critical timely action is required, the second function makes an appropriate force prompt for the pilot.

Vortex Ring State Avoidance

If the vortex ring state could be well defined as a numerical limit, an arithmetically based limit avoidance cueing system would

apply. The HACT Program takes that approach to provide a power settling avoidance cue on the collective control axis. However,

when the condition is not explicitly defined but is generally understood, an expert model assesses the possibility of the condition

and sets tactile avoidance cues and non-tactile cues. This vortex ring avoidance cue treats the condition not as an arithmetic cue as

does the HACT program, but as a logic based cue. While not usually addressed in helicopter operator’s manuals, flight schools

include settling with power as an important topic of instruction. School manuals10 describe the conditions conducive to settling

with power as: a vertical or nearly vertical descent of at least 300 feet per minute, low forward airspeed, and normal-high engine

power (from 20 to 100 percent). From this knowledge (depicted in Figure 8), an abridged fuzzy inference system takes the form:

With the c

simultaneously ac

cue. Again, fligh

rules for the collec

Rat

e of

Des

cent

Mem

ber F

unct

ions

Horizontal Speed Member Functions

AutorotativeBoundary

Descent Angles

90° 80°70°

60°50°

40°

30°20°

Low ETL Cruise Vmax

Low

Stee

pFa

st

Vortex RingState

Rat

e of

Des

cent

Mem

ber F

unct

ions

Horizontal Speed Member Functions

AutorotativeBoundary

Descent Angles

90° 80°70°

60°50°

40°

30°20°

Low ETL Cruise Vmax

Low

Stee

pFa

st

Vortex RingState

Figure 8. Fuzzy Inference of Vortex Ring State.

Operator Fu

IF To

AND Ra

AND H

THEN Vo

zzy Variable Membership Function

rque is Nominal (20-100%)

te of Descent is Steep ( 300-1500 fpm )

orizontal Speed is Low ( <25 kts)

rtex Ring is Imminent

ondition identified, the fuzzy cueing system

ts as a fuzzy controller for an active control axis

t school instruction can form the basis of the

tive and cyclic cues shown below:

The abridged fuzzy logic controller for an avoidance cue when vortex ring state is imminent:

Operator Fuzzy Variable Membership Function

WHILE Vortex Ring is Imminent

AND Altitude is Safe

THEN Collective Cue is Decreasing

Autorotational Entry Prompt

Many high performance rotorcraft use high disk loads and low blade inertia to enhance agility. Such rotor systems loose

rotational speed very quickly when power fails due to engine failure, drive shaft failure, or similar emergencies. In these situations,

the pilot must immediately identify the emergency, reduce collective to maintain rotor speed and enter an autorotational descent.

An autorotational entry prompt assists the pilot by identifying the emergency and providing a downward cue on the collective. If

the pilot had set a friction to the collective, the active control system would disable the friction. If the pilot had removed his hand

from the collective, the force prompt would physically lower the collective far enough to maintain safe rotor speed.

An autorotation entry cueing system would only prompt the pilot to take immediate action. It provides no prompt when its

heuristics deem such action inappropriate. This might occur at

low altitudes (below 100 feet) or when the pilot is already taking

corrective action. Any emergency procedure cue must include

thoughtful rules to prohibit interference with the pilot during

emergency maneuvers. Consequently, in an emergency situation,

the cue arbitration module may disable all limit avoidance cues.

Operator Fu

IF Ω

OR d/

AND Ω&

AND Al

THEN Co

AND FrRoutine Cues

Position Cue for Hover

An active cue hover hold takes the form of a force detent or a light preload

interface module, to physically manipulate the active control inceptor and keep the

control cues, the pilot can immediately override it by applying a force greater th

operate without interfering with the pilot as he maneuvers from one position to a

hover, the pilot feels the cyclic fall into the force detent. He could then release

aircraft stationary at a hover. The rule base for a fuzzy logic controller depends prim

Decoupled Axis Cueing

When the position cue rule set decouples the longitudinal, lateral, and vertical

lateral cyclic, and collective; the fuzzy logic controller provides an axis position cue

such a cue. It guides the pilot when decelerating to and accelerating from a hove

pilot follows the collective cue (or lets the cue physically move the collective), whi

The vertical cue will physically adjust the collective to maintain a constant h

horizontally. Another form of this cue is the vertical mask/unmask cue. Here, th

to physically position the cyclic), while he increases and decreases collective to un

system physically moves the cyclic to maintain the same horizontal position. Th

where the pilot allows the active control system to move the collective and longitud

zzy Variable Membership Function

Rotor is Below Nominal

dt ( ΩRotor/ΩTurbine ) is NOT Zero

Rotor is Decreasing

titude is NOT Low

llective Cue is Decreasing

iction is Zero

Operator Fuzzy Variable Membership Function

WHILE Vortex Ring is Imminent

AND Altitude is Safe

THEN Longitudinal Cue is Decreasing (Forward)

AND Lateral Cue is Increasing (left or right)

force, described in more detail with the tactile

aircraft in a fixed position. As with other tactile

an the cueing force. An active hover cue can

nother. But whenever the aircraft approaches a

the cyclic and allow the hover cue to hold the

arily on position, speed and angular rates.

cues to respectively drive the longitudinal cyclic,

. An energy management cue is an example of

r without altitude loss or gain. In this case, the

le he moves the cyclic away from its hover cue.

eight above ground while the aircraft moves

e pilot follows the cyclic cues (or allows the cue

mask and mask the aircraft. The active control

e final example is the lateral mask/unmask cue

inal cyclic while he changes the lateral position.

Instrument Cues

Spatial Orientation Cueing

During instrument flight, pilots force themselves to ignore outside visual information and proprioceptive perception of the

aircraft’s spatial attitude. “Trust Your Instruments” is the mantra and pilots maneuver the aircraft solely on the basis of the visual

information provided by the artificial horizon, altimeter, turn and slip, and other instruments. Instrument flight sometimes

demands great force of will to ignore the gut feeling that the aircraft is flying awry. Pilots must overcome the combined power of

vestibular and proprioceptive misperception. An active control system can augment the instrument panel indications with

supporting tactile cues. The pilot would then have mutually supporting visual and proprioceptive indications and can more easily

overcome his conflicting vestibular and remaining proprioceptive cues to recognize the true spatial orientation of the aircraft

A spatial orientation cue uses inceptor displacement and force to respectively command rate and attitude. In such a scheme,

the aircraft flight control system accepts physical displacement of the longitudinal and lateral control inceptors as its input for pitch

and roll rate commands. The active control system provides a counter-force as a function of the aircraft’s pitch and bank angles.

So a constant right lateral cyclic displacement would command a constant roll rate, but a pilot’s constant lateral force would

command a constant bank angle. Or alternatively, if the pilot provided no lateral force or took his hands off the control stick, the

active control counterforce would command the displacement and bring the aircraft to a wings level attitude.

Consider the example of an instrument pilot making a right turn. When he initiates the turn from straight and level flight, the

lateral stick is centered and neutral. The pilot moves the stick right and feels the nominal leftward counterforce just as he would

with a passive inceptor. The flight control system recognizes the displaced inceptor and commands the control surfaces to roll the

aircraft right. As the aircraft begins to bank right, the pilot feels the counterforce increase. This force tells the pilot that he is in a

right bank and gives him the intuitive feeling that the aircraft wants to bring itself back level. If the pilot held the rightward

displacement, the aircraft would continue to roll right into a barrel roll and the left counterforce would increase to a maximum

when the aircraft reached a 90° right bank. If the pilot allowed the force to guide him, he would hold a constant right force against

the stick, but the stick would move left to a centered position where the flight control system would recognize zero displacement

and command the aircraft to a zero roll rate and the bank angle would be constant. When the pilot decides to terminate the turn

and return to wings level, he would reduce his force against the stick to zero or simply remove his hand altogether. The active

control system force would physically guide the stick to the left until it reached a zero force position with a left displacement. The

flight control system would recognize the left displacement and command a left roll rate. As the aircraft rolled left towards a wings

level attitude, the active control system recognizes the decreasing bank angle and moves the zero force position of the lateral stick

to the right. This zero force position guides the stick to the right until it reaches the centered position when the bank angle is zero.

Instrument Approach Cueing

Similar active cues can guide the pilot during an instrument approach. An instrument approach active cue moves the zero-

force positions of the active control axes to command the aircraft along the proper glide slope and approach course. The active

control system may have its own approach autopilot system, but some advanced flight control systems already provide an

instrument approach autopilot. Such a flight control system could provide the control displacement algorithm to the active control

system instead of back driving the control position. The pilot can follow the tactile cue while observing the conditions outside the

cockpit. When the aircraft descends below the cloud base, the pilot can immediately transition to visual flight and complete the

approach, which may include timely and delicate maneuvers for low decision heights, circle to land approaches, and other

challenging terminations. A tactile approach cue can ease the sometimes disorienting transition from instrument flight to visual

flight. With effective spatial orientation and instrument approach tactile cues, the pilot could conceivably conduct a no-gyro radar

precision approach blind folded.

Pilot Customization

Passive Customization

An active inceptor can define the nature of the nominal force displacement relationship to suit pilot preferences and needs.

Even passive inceptors allow pilot adjustments though cockpit knobs and buttons for collective and cyclic friction, fore to aft cyclic

centering, and force trim. An active control system allows similar presets through instrument panel interface menus or directly

through switches beside the inceptor. The range and variety of force-feel characteristics vary with the design of the active

controller but commonly include static friction, dynamic friction, stiffness, damping, and range of motion. Passive customization

allows the pilot to select these tactile qualities to suit his preferences.

Active Customization and Pilot Induced Oscillation

Active customization automatically adjusts global force characteristics, primarily damping and stiffness, to ameliorate pilot

aggressiveness, over-controlling, and pilot induced oscillation. Pilot-aircraft dynamic interaction is the basis for Pilot Induced

Oscillation (PIO). An active customization system identifies patterns of unfamiliar or overly aggressive pilot behavior and adjusts

the tactile quality of the cockpit controls. Such flying patterns range from benign over-controlling and porpoising to dangerous

limit-cycle oscillations involving the pilot, the aircraft dynamics, and the control surface actuators. Rigorous design standards11 for

aircraft and flight control systems eliminate the latter, dangerous form of PIO. An active control system can potentially eliminate

or ameliorate the remaining forms of PIO.

A fuzzy logic inference system can identify pilot behaviors indicative of PIO, such as a sudden increase in power in the time

history of control and state frequency content12. Once identified, the system can positively affect the time-constant of the physical

interaction between the pilot and the inceptor. The natural frequency and damping define the apparent inertia of the inceptor and

can make the aircraft feel more or less responsive. It gives the pilot a sense of how rapidly the aircraft can react to his movements.

This is analogous to the different experiences felt from wagging a pencil in hand compared to the feeling of wagging a brick.

Cue Arbitration Module

With multiple critical control position vectors, ucrit, for multiple limits and logical cue positions across multiple control axes,

the design needs an arbitrator to decide which cue among many will drive each style of tactile cue for each of the active control

axes. This module defines the cue position, ucue, which the tactile interface module will use. In most instances, the solution is the

most conservative cue for each control axis. But it is not always appropriate to cue every control for the most conservative limit.

At times the cues may conflict with one another as when one limit is exceeded because a control axis is too far left while another

limit is exceeded because the same control is too far right. In such cases, the arbitrator uses a rule-based method of de-confliction

and appropriate cue selection. Depending on the precision or confidence of the control cue, the arbitration module may command

an abrupt step force cue for high confidence limit predictions or a more gradual cue for less well-defined or low confidence

predictions.

Most Conservative of Several Limit Cues

Arbitrating among multiple cues may be very simple. Usually the most conservative control position of the multiple limits

should drive the tactile cue. For example, consider a moment of forward flight when the longitudinal cyclic position is forward at -

5%. The Critical Control Positions for two limits are 30% aft for vertical load limit and 45% aft for the main rotor blade stall limit.

The most conservative method chooses 30% aft as the combined critical control position.

Control Axis Selection

When each limit is invariably mapped to a primary control axis, the most conservative method is straightforward and simple.

But a limit’s relationship to the control axes changes with the flight condition, a fuzzy inference system decides which control axes

are most appropriate as cues for each limit and whether the cue should be tactile or non-tactile. The system eliminates the cue or

limits it to a subset of permissible positions. The primary indicator of an appropriate control axis is the dynamic nature of the

critical control position for the axis. When the critical position changes rapidly across a large range of control positions, it is less

desirable as a tactile cue. This generally occurs when the limit sensitivity is low with respect to the control or when the limit varies

rapidly due to the states. In such a case, the limit is too fast to cue. A force cue seems jittery and unpredictable and the pilot is

likely to find it objectionable. In this case, the limit arbitrator entirely eliminates the cue from the control axis.

A second form of selection restricts the cue to a subset of the control positions. The rules that shape this inference system

rely on the knowledge interrelated limits and the consequences of control positions. These rules are specific to the relevant limits

of the prediction module. Examples of interrelated limits are vertical loading and main rotor blade stall. When the aircraft

approaches those limits together, as in a pull up maneuver, both avoidance cues would push the longitudinal collective forward. In

extreme cases, the cue would push the collective forward and put the aircraft into a dive that would exacerbate the problem.

Intelligent Selection of Conflicting Cues

The cue arbitration module uses a continuum of fuzzy logic assigned weights to emphasize or ignore each active control axis.

This effectively prioritizes the urgency of the limits. In cases when the aircraft flies beyond two or more limits simultaneously, the

critical control positions may be conflict. The critical control position for one limit may be above the current position while the

critical position for different limit may be below the current position. The arbitrator emphasizes the cue for the most urgent rule.

Tactile Interface Module

After the cue arbitrator decides the one set of cue positions, the tactile interface converts the information into intuitive force

cues for the pilot. In general, the force cue is a function of the cue positions from the cue arbitration module, the position of the

controls, the velocity of the controls, and the time. The cueing force is a combination of the nominal force displacement curve,

softstops, the detents, oscillations, damping and natural frequency response. Because human pilots have different degrees of

strength and control for the different control axis, it is appropriate to decompose this function into its active control axis

components and tailor them to pilot physiology.

( ) fricj

ji

issnomcue FFFFFF,tu,u,uFFn

+++++== ∑∑ ζωωdet& ( 15 )

Nominal Force-Displacement Relationship (Fnom)

An inceptor uses a nominal force-displacement relationship where the pilot feels a centering force that increases gradually and

nearly linearly as it is pushed away from its neutral position.

( ) onom FmuuFF +== ( 16 )

The zero-force intercept is the neutral position where the inceptor will settle when left untouched. An active sidestick can

offer cues and guidance by changing the zero-force position and how the counter-force increases as pilot applies force. The force-

displacement relationship can be nearly flat, m=0. This is the typical feel of a traditional helicopter cyclic stick without friction.

Another relationship uses a preload force. With a preload, the control will not move from the neutral position until the breakout

force is reached.

Another significant choice is the inceptor range of displacement. Traditional cyclic sticks move several inches in two axes.

An active sidestick may move approximately 25 degrees or more longitudinally and laterally and may provide a third axis in the

twist about a vertical axis. Smaller ranges of movement, such as 5 or 10 degrees from neutral, are useful when force is the only

interaction between the pilot and the active system. Larger ranges of movement, such as 15 to 30 degrees from neutral, allow both

force and displacement as information channels between the pilot and the active control system. However, very large ranges of

movement in a sidestick can be awkward for the pilot. Also, a larger range magnifies the movement of limit avoidance cues to the

point where they may be objectionable to the pilot. The range of movement may best be left adjustable for pilot preference.

Control (inceptor) Travel

App

lied

Forc

e

Force Detentfor guidance

Force Detent

Force

uF

∆∝

1

Force Cue

How it feels

ForceDisplacementRelationship

“Ste

epne

ss”

Control (inceptor) Travel

App

lied

Forc

e

Force Detentfor guidance

Force Detent

Force

uF

∆∝

1

Force Cue

How it feels

ForceDisplacementRelationship

“Ste

epne

ss”

Figure 9. Force Inverse to Control Margin

with Detent .

App

lied

Forc

e

How it feels

Inverse Detentfor avoidance

InverseForce Detent

Step ForceAt ucrit

Force Cue

Control (inceptor) Travel

ForceDisplacementRelationship

“Ste

epne

ss”

App

lied

Forc

e

How it feels

Inverse Detentfor avoidance

InverseForce Detent

Step ForceAt ucrit

Force Cue

Control (inceptor) Travel

ForceDisplacementRelationship

“Ste

epne

ss”

Figure 10. Step Force at Critical Position

with an inverse detent.

Force Inversely Proportional to Control Margin (Fss)

One tactile cueing method is the use of a Force Inversely Proportional to

Control Margin (Figure 9). This form of a softstop, used successfully with V-22

simulations, creates a counter force that opposes the pilot as he pushes the

control towards a limit. The magnitude of the counterforce is approximately

inversely proportional to the control margin and increases to a maximum

counter-force at the critical control position. This method can be implemented

with minor variations, but its defining characteristic is the gradual increase in

counter-force as the critical control position is approached. This method does

not provide a decisive cue regarding the limit and this reflects the true indistinct

nature of many (perhaps most) limits, which are based on subjectively defined

safety margins added to structural failure loads or control system domain

boundaries.

Step Force at Critical Control Position (Fss)

Another successful form of softstop uses a step increase in counter-force

at the critical control position (Figure 10). This is the primary cueing method for

the RASCAL active control system because it provides a decisive indication to

the pilot about the location of the edge of the flight envelope defined by the

limit prediction algorithms. However, when the critical control position varies

rapidly while the pilot is following the cue, it can seem jittery and may be

objectionable.

Detents and Inverse-Detents (Fdet)

A force detent superimposed on the nominal force-displacement

relationship serves well as a trim cue or an autopilot cue. The sidestick will

remain in a detent “force-well” until the pilot provides a sufficient break away

force (Figure 9). Then the stick would follow the nominal force-displacement

relationship. The inverse detent has the opposite effect (Figure 10). It pushes

the stick away from the inverse-detent position to one side or the other. Such a

cue steers the pilot away from high-risk flight conditions, such as very steep, high

power approaches where vortex-ring state is predicted as imminent.

Shaking and Vibration (Fω)

Shaking and vibration is a very useful supplemental cue. It is used to indicate that the aircraft is already beyond a limit. It can

also cue impending limits whose indications involve vibration. For example, a high frequency vibration can cue loss of tail rotor

effectiveness and tail rotor malfunctions. A low frequency, 1/rev, can cue main rotor stall and other main rotor limits.

( ) ( )tAtFF ωsin== ( 17 )

Damping and Natural Frequency Response (Fωnζ)

The frequency response of an active inceptor can imply agility or sluggishness to convey the maneuvering capability of an

aircraft in varying flight regimes. Damping as a force cue, can be very effective for transient limits such as maximum flapping with

respect to cyclic. It is the only force cue listed here that depends directly on control speed. Maximum transient limits depend

primarily on fast control movements rather than control positions.

( 18 ) )2( 2uuuMF nnnωςωςω ++= &&&

Friction (Ffric)

Friction is a constant force that opposes the direction of movement. It may have use as a cue, but mainly it helps the pilot

hold the control at a constant position despite airframe vibrations or those occasions when the pilot removes his hand.

Applications

Limit Avoidance Cueing for the RASCAL Helicopter

Prototype development for the RASCAL active control system with the Sterling Dynamics Active Sidestick System model

SA-S-2D-1 began in the summer of 2001 at the Army/NASA Rotorcraft Division within the Real-Time Interactive Prototype

Technology Integration/Development Environment (RIPTIDE), which the Rotorcraft Division designed for just this sort of

project13. The RIPTIDE uses SIMULINK as a control system development tool

and provides a simulation environment using any of several math models,

including the UH-60A general helicopter (GenHel) model. The first application

emulated the previous success of the HELMEE project. The active control

system had the structure depicted in Figure 11. No logical cues were used. The

Main Rotor Stall limit was defined numerically as Equivalent Retreating Indicated

Tip Speed (ERITS).

Relevant LimitMain Rotor Blade Stall

Limit prediction methodStatic Neural Network with Complementary

Filter correction

Type of predictionFixed Time Horizon

LimitPrediction

Limit-to-Control Partial Derivative

Critical Control

Calculation

Most Conservative Cue(direct single axis cue)

IntelligentCue

Arbitrator

TactileInterface

Step force at critical positionShaking (beyond limit)

Figure 11. Main Rotor Blade Stall Cueing

oz

eqo

WW

N

VR

−Ω

=ρρ

ERITS ( 19 )

The prediction model was the same polynomial static neural network

developed for the HELMEE study. It provided a prediction for a fixed time

horizon of 0.253 seconds. A complementary filter between the neural network

and the instantaneous ERITS value eliminated steady state prediction error.

ERITS values below 250 were considered beyond the limit, and were signaled by a

shaker cue. An ERITS prediction of 300 defined the placement of the softstop.

( 20 ) )(300lim ERITSfpsy =−

Time (seconds)66 68 69 70 71 72 78 79 80 81 82 83

Time (seconds)

Long

itudi

nal s

tick

(deg

)

(Fwd) -25

(Aft) 25

0

ERIT

S (f

ps)

800

600

400

200ylim = 300 ylim = 300

Stick Position, δCritical Position, δcrit

Stick Position, δCritical Position, δcrit

Actual ERITS, yPredicted ERITS, yp

Actual ERITS, yPredicted ERITS, yp

Stick Position, δCritical Position, δcrit

Stick Position, δCritical Position, δcrit

Actual ERITS, yPredicted ERITS, yp

Actual ERITS, yPredicted ERITS, yp

Pilot overrides blade stall limit cue Pilot follows the blade stall limit cue

Figure 12. Main Rotor Blade Stall limit avoidance cueing in piloted RIPTIDE simulation.

The performance of the active control system in piloted simulation* of two consecutive pull-up maneuvers is shown in Figure

12. The position of the softstop and stick are shown in the top graphs. The predicted control margin is the area below the

softstop (in red) and above the stick position (in black). In both maneuvers, the aircraft begins in an accelerating dive where the

limit parameter, ERITS, is approaching its limit. Consequently, the control margin is narrowing. When the predicted ERITS

reaches its limit as the stick moves aft, the pilot encounters the softstop cue. In the first maneuver (at left), the pilot overrides the

cue to make an abrupt pitch up. He exceeds the limit as ERITS drops to 175 fps. At critical times, the pilot may need to do this to

avoid sudden obstacles (i.e. wires) and an active cue does not prevent him. In the second maneuver (at right), the pilot encounters

and follows the cue, and in so doing gets the most out of the maneuver envelop without significantly exceeding the limit.

* This piloted simulation is available as a QuickTime movie at the author’s website, http://wilbur.ae.gatech.edu/

Modular Applications and Technology Transfer

The five modules of the design are individually useful outside of this active control system. Limit prediction systems such as

those in the limit prediction module are useful for remotely piloted vehicle (RPV) and unmanned aerial vehicle (UAV) applications.

Those vehicles, typically much smaller than manned aircraft, have dynamics too quick for remote pilot avoidance and require

automated limit avoidance systems. Limit prediction systems integrated in the vehicle flight control systems keeps the RPV or

UAV within its structural and controllability limits. These limit prediction and avoidance systems operate without intervention

from the remote pilot, but allow that pilot to maximize the vehicle’s maneuver envelope. The logical cues module combines

intelligent control systems useful for various autonomous robot applications, especially those that control three-dimensional

maneuver, such as UAVs and autonomous submersibles. The fuzzy inference systems for emergency procedures are useful for

non-tactile cues in cockpits without active systems.

The converse is also true. The limit prediction methods and avoidance techniques developed for unmanned aerial vehicles

can improve the limit avoidance cues in manned aircraft. Independently developed fault detection and identification systems may

be transferable to the logical cues module in an active control system. The intelligent navigation and search algorithms developed

for robotic aerial vehicles may be entirely transferable to the logical cues module. Intelligent controllers integrated into the active

control system, will make single pilot cockpits more practical for future military and commercial aircraft and they make the active

control system an increasingly capable robot assistant.

Relevant LimitWheel Slipping

Prediction methodMath Model, yp=y

Type of predictionFixed Time Horizon

Limit Prediction Modules

Emergency Cue –Counter-Steer for Skid

Logical Cues

Local SensitivityDirect, ucrit=uo

Critical Control Calculations

PrioritizationIntelligent Cue Arbitrator

TactileInterface

Step force at critical positionShaking (beyond limit)

Relevant LimitLateral Skidding

Prediction methodStatic Neural Net

Type of predictionFixed Time Horizon

Limit-to-Control Partial Derivative

Figure 13. Active Control Design – An Automotive Application

An Automotive Application

Although the initial application of the design is for a helicopter active

control system, the open architecture readies it for other aircraft and

considerably different environments and applications. Consider a conceptual

design for tactile cueing in automotive applications. Drive-by-wire systems

have emerged for commercial applications, primarily replacing the cable

throttle to improve fuel efficiency and meet emission standards. Concept cars,

such as Daimler Chrysler's R-12914, demonstrate drive-by-wire steering systems

with sidestick controllers. The sidestick uses fore and aft longitudinal to

command throttle and braking. The lateral axis steers.

Anti-lock braking is a common automatic limit mechanism that provides

a tactile cue in the brake pedal back-driven from the mechanical system. An

active limit avoidance cue could use the same slip detection system already in

use. The open architecture allows customized slip prediction systems for high

performance models. A more sophisticated slip prediction system could

provide a more effective avoidance cue using state information such as

acceleration, tire rotation, tire condition, and other relevant data. For the brake

lock cue, the aft critical control position is the location for an aft softstop.

Depending on the reliability of the cue, the softstop could be a step force at the

critical position or a force inversely proportional to the control margin. A

forward softstop cues for forward wheel slipping due to high throttle. This cue

prevents the driver from unintentionally spinning wheels on ice, gravel or

pavement. As with all tactile cues, the driver can still override it to burn rubber.

When drivers take tight corners taken at high speed, especially on loose or slippery surfaces, the wheels may lose traction,

sending the car out of control. A static neural network prediction driving a lateral softstop provides a limit avoidance cue for lateral

skid. Again, the detail and fidelity of the limit prediction system can be customized for various models or omitted altogether.

While the previous two cue systems were arithmetically based (limit) systems, a counter-steering cue is a logic-based cue that

provides an emergency procedure prompt for the driver. A fuzzy logic inference system using vehicle state information such as

lateral acceleration, velocities, and angular rotation, recognizes that the vehicle has lost traction and has begun to skid. Based on of

expert heuristics for handling a car in a skidding situation, the fuzzy logic controller provides a counter-steering softstop cue into

the skid to arrest the condition. For more advanced stages of a skid, the controller may command other directional cues depending

on its rule-base. The limit arbitrator prioritizes this cue among the others and de-conflicts them if necessary.

Conclusion

The holistic approach of this active control system design treats the tactile cueing system as a whole, self-contained control

system rather than subsystem of the aircraft flight control system. The approach requires and enables the active control system to

function properly despite large-scale uncertainties and to adapt to changing aircraft dynamics and flight control system

combinations. Intelligent control techniques, especially adaptive neural networks and fuzzy logic inference systems, within the

overall design, provide that adaptability. The open architecture of the design facilitates customized tactile cueing systems that can

incorporate proven systems; such as the main rotor blade stall avoidance cue demonstrated a RIPTIDE based blade stall limit

avoidance cueing system.

Arithmetically based limit avoidance cues are a prominent feature in this design and several developments improve limit

predictions and critical control position calculations. The design recognizes that any prediction must include a future transition

assumption to establish a causal prediction model. The choice of this assumption strongly affects the precision and quality of the

prediction. An example of an adaptive method shows the application of adaptive neural networks to limit prediction, specifically

transient limit prediction. And most importantly, a newly developed predicted limit search algorithm finds the critical control

position without assuming a linear, bijective character of the limit-to-control relationship.

Significantly, this design recognizes and accommodates logically based cues as well as arithmetically based cues. This opens

the design to knowledge based procedural cues and robotic assistance. These types of cues share a technological base with

autonomous systems and benefit from unmanned aerial vehicle research and development. Novel applications illustrate the utility

of logical cues. Emergency procedure prompts guide the pilot when immediate critical action is required. Autopilot and auto-

navigation systems provide robotic assistance to reduce pilot workload and can allow hands-off flight control for in-flight

navigation and station keeping. Tactile spatial orientation cues augment cockpit panel displays to present the pilot with critical

instrument information. Active adaptation of the dynamic character of the inceptor can ameliorate over-controlling behaviors and

pilot induced oscillations.

This open design for helicopter active control systems maintains design flexibility as far as possible. It can serve on other

platforms including other types of aircraft, automobiles, and submersibles. The five modules are individually useful for applications

in the fields of haptics, remotely piloted vehicles, simulations, gaming, and human systems modeling among others.

Acknowledgments

This work is carried out under grant NAG 2-1418 originating at the Army/NASA Rotorcraft Center at NASA Ames. This

project is funded by a research grant from the U.S. Army Aeroflightdynamics Directorate for in-flight demonstration of tactile

cueing on the Rotorcraft Aircrew Systems Concepts Airborne Laboratory (RASCAL). Future development in this design includes

a formalized and flexible information interface between the functional modules and additional practical applications and continued

development in RIPTIDE and RASCAL through the summer of 2002. The active control system will be evaluated in piloted flight

onboard the RASCAL helicopter over the winter and spring of 2002/2003.

References

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14 World Wide Web article: http://www.daimlerchrysler.com/index_e.htm?/specials/sidestick/sidestick1_e.htm