A New Generation of Adaptive Control: An Intelligent Supervisory Loop Approach A Dissertation Proposal Presentation By Sukumar Kamalasadan Department of Electrical Engineering and Computer Science, University of Toledo, 30th April 2003 A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Dynamic Systems Operates in Real time Specifies Performance Quality Regardless of External Disturbance Complex Dynamic Systems Uncertainties: Functional and Parametric Time Varying and/or nonlinear elements

The Control Challenge, A Practical Matter : Practical Systems are mostly Nonlinear and Shows some degree of Uncertainty Advances in technology led to highly complex processes, to be controlled with tight specifications and high level of autonomy Example: Fighter Aircrafts

Practical Approaches to the Control Design Problem Systems that can be modeled “Adequately” with stationary Linear Models: Fixed Parameters (Stationary) Controllers Designed off line. Mostly used for Linear Time Invariant Systems Systems that CANNOT be modeled “Satisfactorily” with stationary Linear Models: Adaptive Controllers (STR and MRAC) Sophistication Level # 1 Intelligent Adaptive Controllers (A New Generation) Sophistication Level # 2 Which Implies Certain Levels of Learning and Adaptation

Research Motivation Investigate possibilities of some Intelligence based solutions to a major structural problem that exists in the two “conventional” Adaptive Control techniques (MRAC & STR ): The Problem: The Designer’s A priori Choices, such as the choice of a “MODEL” as required in either of the two Schemes Inability to Control functionally nonlinear and Changing systems A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Intelligent Adaptive Control What is Intelligent Control ? Controls complex uncertain systems within stringent specification Features Ability to Learn: Ability to modify behavior when condition changes Ability to Adapt: Ability to handle uncertainty by continuously estimating the relevant unknown knowledge Ability to deal with Complex Systems : Characterized by nonlinear dynamics and multiple mode of operation Autonomous in Nature: Ability to deal with uncertainty all by itself without human intervention A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Intelligent Adaptive Control : Constituents Deals with linear or nonlinear parametric uncertain Systems Needs detailed prior knowledge of the systems to be controlled Have the ability to adapt Artificial Intelligence (AI) Techniques Neural Networks Ability to learn either offline or online Adjusts the parametric values allow the network to learn Fuzzy Systems Ability to fuzzify a complex system in terms of linguistic rules Can avoid dealing with complex mathematical models Create the “long term memory” or learning behavior Reduce the uncertainty in dealing with models

Intelligent Adaptive Control : Applications Objective Control of Complex systems which is affine but shows “ Multi Modal” and Sudden parametric ‘Jumps’ Control of Nonlinear Systems which shows “Functional Uncertainty” Control of Nonlinear Systems which shows “Functional Uncertainty” and “Multi Modal”

Statement of Dissertation Objectives Theoretical Design and Development of Three Intelligent Adaptive Control Schemes Develop an F-16 Aircraft Model in MATLAB for Investigation and Application Classification Development of the F-16 Aircraft MATLAB Model Fuzzy Switching Multiple Reference Model Adaptive Control Scheme Neural Network Adaptive Control Scheme Neuro-Fuzzy Adaptive Control Scheme

Current Status of Dissertation Development of a 6 Degree of Freedom (6 DOF) dynamic F16 Aircraft Model in MATLAB and SIMULINK Development of a Fuzzy Switching Multiple Reference Model Adaptive Controller Development of a Neural Network Adaptive Controller Development of Neuro-Fuzzy Adaptive Controller Overall Dissertation Status

Concluding Remarks Three Intelligent Adaptive Control schemes are proposed Objective is to control a class of multimodal nonlinear systems which deals with function and/or parametric uncertainty Application systems which shows changes influenced by external or internal disturbance A nonlinear Aircraft Model is developed to simulate as an appropriate application system, and to investigate and verify the effectiveness of schemes Preliminary Simulation Results appear to be promising

Typical Stationary Controller Regulator Plant Control Signal Command Signal y Output Parameters Control processor A stationary (Fixed Parameter) Controller is designed ( Off Line ) For The Plant as represented by a Stationary M odel

Self Tuning Regulator (STR) Scheme

Model Reference Adaptive Control (MRAC) Scheme

Development of the F-16 Aircraft MATLAB Model Developing the Building Blocks Developing the Algorithm in MATLAB including the subroutine functions and the main equations of motions Testing with certain developed Trim conditions Developing the SIMULINK Model 

F-16 Aircraft Body System Axes and Variables

F-16 Aircraft Model Building Blocks Aerodynamic Model 6DOF Equations Of Motion Actuator Modeling Atmospheric Model Engine Model Control deflections

Development of the F-16 Aircraft MATLAB Model Computing Air data Outputs: - Mach number, Dynamic Pressure Inputs: -Velocity, Altitude Aerodynamic look-up table and coefficient buildup Outputs: - Aerodynamic Force (Cxt, Cyt, Czt) & Moments (Cnt, Clt, Cmt) coefficients Inputs: -Control Variables (elev, ail, rdr) and (alpha, beta) Computing Engine Model Outputs: - Engine Thrust Inputs: -Power, Altitude, Mach Number State Equations Force Equations Derivative, Inputs: -Moment Rates (P, Q, R), Velocity (UVW), Kinematics (Phi, Theta) and Aerodynamic Force coefficients Outputs: - Vt, Alpha and Beta Derivatives Kinematic Equations Derivative, Inputs: -Moment Rates (P, Q and R), Kinematics (Phi and Theta) Outputs: - Phi, Theta and Psi Derivaties Moments Equations Derivative, Inputs: -Moment Rates (P, Q, R), Aerodynamic Moment Coefficient (Clt,Cmt.Cnt) and Inertia Constants Outputs: - Moments Derivatives Navigation Equations Derivative Inputs: -Moment Rates (P, Q, R), Aerodynamic Moment Coefficient (Clt,Cmt.Cnt) and Inertia Constants Control Vector

Development of Steady State Trim Conditions Trim Conditions are Developed based on a Simplex Routine Table Below Shows the Trim conditions for five cases Conditions Variables Nominal Xcg=0.38C VT(ft/sec) 502.0 5020 (rad) 0.03691 0.03936 0.03544 0.2485 0.3006 (rad) -4.0E-9 4.1E-9 3.1E-8 4.8E-4 4.1E-5 (rad) 1.367 (rad) 0.05185 P(rad/sec) -0.01555 Q(rad/sec) 0.2934 0.3000 R(rad/sec) 0.06071 Thtl(0-1) 0.1385 0.1485 0.1325 0.8499 1.023 El(deg) -0.7588 -1.931 -0.05590 -6.256 -7.082 Ail(deg) -1.2E-7 -7.0E-8 -5.1E-7 0.09891 -6.2E-4 Rdr(deg) -6.2E-7 8.3E-7 -4.3E-6 -0.4218 0.01655 Reference Models (90S+287) (S3+20.87S2+115S+28) (110S+287) (S3+20.87S2+115S+287) (235S+4163) (S3+40S2+608S+4163) (10S+287) (S3+20.87S2+115S+287 (132S+287)

Developed SIMULINK model of F16 Aircraft

 Proposed Scheme I Scheme Outline Developing the Model Reference Control Law Development of Reference Models for each operating modes Testing the operation by manually switching the Reference Model Developing the Fuzzy Logic Scheme depending on the System Testing overall system with the Dynamic Fuzzy Switching Scheme  A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Fuzzy Logic Switching Scheme (FLSS) Proposed Scheme I : Ref. Model 1 Ref. Model 2 Ref. Model n Command Signal Control Signal Aux. Inputs y Regulator Parameters Error Fuzzy Logic Switching Scheme (FLSS) Output + Regulator Plant Adjustment Mechanism -  A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

MRAC Structure Develops a Control Law looking at the Input and Output of the Plant Updates the Control law using an Adaptive Mechanism Use a reference model to effectively model the dynamics and forces the plant to follow that model

 Proposed Scheme II Scheme Outline Design of the Dynamic Radial Basis Neural Network (RBFNN) Development of overall scheme linking the RBFNN control with Adaptive Control Testing the Scheme on a Functionally Nonlinear System  A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Neural Network Controller Proposed Scheme II + Usl - Umr Unn em ym yp Neural Network Controller Nonlinear Process MRAC Controller Adjustment Mechanism  Reference Model A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Features of Proposed Neural Network Centers, Radius and Distance adapt with time looking at input vector Dynamic in Nature Starts with three nodes and grows depending on functional complexity Grows accordingly RBFNN weights adjust to correct the Output and Reference Error Learns Online Features Neural Network Radial Basis Function Neural Network

RBFNN Structure Consists of Nodes in Input layer Nodes basically have two elements : Center and Radius Consists of a basis function which is a Gaussian Function The output is the summation of each functions times the weights

Proposed Scheme II (Nonlinear Functional Uncertain System) Highlights RBFNN Center Grows depending on new Inputs Moves close to Input Set Radius : Changes for each center addition Weights: Adapts Depending on the Error MRAC Stable Direct Model Reference Framework Sliding Mode Gain and Rate Increase Reduces Network Approximation Error Reduces Parametric Drift especially in the Boundary Region  A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

 Proposed Scheme III Scheme Outline Design of the RBFNN Control Design of Fuzzy Logic Scheme depending on the System Development of the Reference Model Integrating overall scheme Testing the system on a Functionally Nonlinear Parametrically Uncertain System  A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Neural Network Controller Proposed Scheme III + Usl - Umr Unn em ym yp Neural Network Controller Nonlinear Process MRAC Controller Adjustment Mechanism  Reference Model ‘1’ Reference Model ‘2’ Reference Model ‘n’ : Fuzzy Logic Switching Auxiliary Inputs Reference Input Desired Inputs A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Proposed Scheme III (Nonlinear Complex System) Flight Control System Sensor Measurements Pilot Command Reference Measurements Controller Output (Thtl,Rdr,Elev,Ail)

Proposed Scheme III (Nonlinear Complex System) Neural Network Flight Pattern Model  Adaptive Control Adjustment Mechanism Fuzzy Switching 

Status: Nonlinear F16 6DOF Model in MATLAB and SIMULINK Developed the Building Blocks of the Aircraft Model Developed 6 DOF nonlinear Aircraft Model Developed Steady State Trim Conditions Algorithmic Development in MATLAB has completed Developed Graphical Equivalent in the SIMULINK  A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Status : Scheme 1 and Simulation Results Problem Formulation has been established Derived a Stable Model Reference Adaptive Law Developed a Fuzzy Logic Switching Scheme Developed a Multiple Reference Model suitable for all ‘modes’ Simulation Results for a Linear ‘Jump’ System Simulation Results of the Pitch Rate Control of F16 Aircraft 

Status: Scheme 2 and Simulation Results Problem Formulation has been established Developed a RBFNN Architecture which is dynamic in nature Derived a Stable Adaptive Law and developed an overall system Simulation Results to control a Nonlinear Process Application of the Developed scheme to control F16 aircraft Dynamics is yet to be accomplished 

Status : Scheme 3 and Simulation Results Problem Formulation has been established Developed an dynamic RBFNN Architecture Development of a Fuzzy Logic Switching Scheme is yet to be accomplished Development of a Multiple Reference Model suitable for all ‘modes’ is yet to be done Integration of Overall Scheme is yet to be done Application to a Nonlinear Process and F16 Dynamics Control is yet to be done 

Proposed Scheme I (Linear Parametric “Jump” System) A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Proposed Scheme I ( Linear Parametric “Jump” System) Time T <40 T<70 T<100 Plant Structure 1/(s2+30s-10) 1/(s2+3s-20) 1/(s2+9s-30) Reference Structure By FLSS 5/(s2+3.51s+1.74) 5/(s2+4.46s+4.11) 5/(s2+7.23s+4.95) I Time T <40 T<70 T<100 Plant Structure 1/(s2+9s-30) 1/(s2+30s-10) 1/(s2+3s-30) Reference Structure By FLSS 5/(s2+7.23s+4.95) 5/(s2+3.51s+1.74) 5/(s2+5.57s+6.32) II Time T <40 T<70 T<100 Plant Structure 1/(s2+18s-20) 1/(s2+24s-10) 1/(s2+9s-30) Reference Structure By FLSS 5/(s2+4.62s+4.93) 5/(s2+3.51s+1.74) 5/(s2+7.23s+4.94) III A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Proposed Scheme I ( Linear parametric “Jump” System) A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Proposed Scheme I ( Linear parametric “Jump” System)  A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Proposed Scheme I ( Pitch Rate Control of F-16 Aircraft)

Proposed Scheme I ( Pitch Rate Control of F-16 Aircraft)

Proposed Scheme I ( Pitch Rate Control of F-16 Aircraft)

Proposed Scheme I ( Pitch Rate Control of F-16 Aircraft) 

Proposed Scheme II (Nonlinear Functional Uncertain System) Desired Position Actual Position Neural Network Inversion Desired Other States Single Link Robotic Manipulator with Payload A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Proposed Scheme II (Nonlinear Functional Uncertain System) Position Trajectory Time A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Overall Dissertation Status A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach

Overall Dissertation Status (Contd.)  A New Generation of Adaptive Control : An Intelligent Supervisory Loop Approach