Neuro-Fuzzy Control of Industrial Systems With Actuator Nonlinearities,9780898715057

Neuro-Fuzzy Control of Industrial Systems With Actuator Nonlinearities

by ; ;
ISBN13: 9780898715057
ISBN10: 0898715059
Format: Hardcover
Pub. Date: 2001-12-01
Publisher(s): Society for Industrial & Applied
List Price: $107.73

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Summary

Neural networks and fuzzy systems are model free control design approaches that represent an advantage over classical control when dealing with complicated nonlinear actuator dynamics. Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities brings neural networks and fuzzy logic together with dynamical control systems. Each chapter presents powerful control approaches for the design of intelligent controllers to compensate for actuator nonlinearities such as time delay, friction, deadzone, and backlash that can be found in all industrial motion systems, plus a thorough development, rigorous stability proofs, and simulation examples for each design. In the final chapter, the authors develop a framework to implement intelligent control schemes on actual systems. Rigorous stability proofs are further verified by computer simulations, and appendices contain the computer code needed to build intelligent controllers for real-time applications.

Table of Contents

Preface xiii
Background on Neural Networks and Fuzzy Logic Systems
1(12)
Neural networks
1(7)
Two-layer neural network topology and tuning
1(3)
Single-layer neural networks
4(2)
Universal approximation property of neural networks
6(2)
Fuzzy logic systems
8(5)
Membership functions and defuzzification
9(1)
Universal approximation property and tuning of fuzzy logic networks
10(3)
Background on Dynamical Systems and Industrial Actuators
13(16)
Dynamical systems
13(8)
Continuous and discrete dynamical systems
13(2)
Mathematics and system properties
15(3)
Lyapunov stability analysis
18(3)
Industrial actuators
21(1)
Friction
22(2)
Deadzone
24(2)
Backlash
26(3)
Neurocontrol of Systems with Friction
29(24)
Neural network approximation of functions with discontinuities
29(10)
Neural network approximation of continuous functions
30(1)
Neural network approximation of functions with jumps
30(5)
Augmented multilayer neural networks for jump function approximation
35(1)
Simulation of the augmented multilayer neural network
36(3)
Neurocontroller for friction compensation
39(11)
Robot arm dynamics
40(2)
Neural network controller
42(6)
Simulation of a neural network controller
48(2)
Conclusions
50(3)
Neural and Fuzzy Control of Systems with Deadzones
53(38)
Introduction to deadzone control
53(1)
Position tracking controller with neural network deadzone compensation
54(16)
Neural network deadzone precompensator
54(6)
Robot arm dynamics
60(1)
Design of tracking controller with neural network deadzone compensation
61(6)
Simulation of neural network deadzone compensator for robotic systems
67(3)
Fuzzy logic discrete-time deadzone precompensation
70(13)
Fuzzy logic discrete-time deadzone precompensator
73(4)
Discrete-time fuzzy logic controller with deadzone compensation
77(4)
Simulation results
81(2)
Appendix: Proof of Theorem 4.3.1
83(8)
Neural Control of Systems with Backlash
91(34)
Introduction to backlash control
91(1)
Continuous-time neural network backlash compensation
92(13)
Backlash inverse
93(1)
Dynamics and control of nonlinear motion systems
94(2)
Neural network backlash compensation using dynamic inversion
96(7)
Simulation of neural network backlash compensator
103(2)
Discrete-time neural network backlash compensation
105(10)
Discrete-time backlash nonlinearity and backlash inverse
107(1)
Dynamics and control of a nonlinear discrete-time system
108(2)
Discrete-time neural network backlash compensation using dynamic inversion
110(3)
Simulation results
113(2)
Appendix: Proof of Theorem 5.3.2
115(10)
Fuzzy Logic Control of Vehicle Active Suspension
125(14)
Introduction
125(2)
System model and dynamics
127(3)
Backstepping-based fuzzy logic controller
130(6)
Outer loop design for ideal force input
130(3)
Inner backstepping loop design
133(3)
Simulation results
136(3)
Neurocontrol Using the Adaptive Critic Architecture
139(12)
Introduction
139(2)
Dynamics of a nonlinear system
141(1)
Adaptive critic feedback controller
142(3)
Adaptive critic neurocontrol architecture
142(3)
Simulation results
145(2)
Conclusions
147(2)
Appendix: Proof of Theorem 7.3.1
149(2)
Neurocontrol of Telerobotic Systems with Time Delays
151(20)
Introduction
151(2)
Background and problem statement
153(2)
Identification of time-delay-free systems using recurrent neural networks
155(2)
Recurrent neural network predictive control strategy for compensating time delays
157(3)
Review of the Smith predictor
158(1)
Control structure
159(1)
Recurrent neural network predictive control and stability analysis
160(3)
Simulation example
163(3)
Conclusion
166(1)
Appendix: Proof of Theorem 8.3.1
167(1)
Appendix: Proof of Theorem 8.5.1
168(3)
Implementation of Neural Network Control Systems
171(16)
PC-PC real-time digital control system
171(7)
Hardware description
172(2)
Software description
174(2)
State-variable data management
176(2)
ATB1000 U.S. Army tank gun barrel testbed
178(1)
Derivation of neural net control system for flexible systems
179(4)
Flexible-link robot dynamics
180(1)
Singular perturbation approach
181(1)
Neural network control algorithm
182(1)
Implementation of neural network controller on ATB1000 testbed
183(3)
Proportional-plus-derivative control
185(1)
Proportional-plus-derivative control plus neural network
185(1)
Conclusions
186(1)
Appendix A C Code for Neural Network Friction Controller 187(14)
Appendix B C Code for Continuous-Time Neural Network Deadzone Controller 201(18)
Appendix C C Code for Discrete-Time Neural Network Backlash Controller 219(10)
Appendix D Versatile Real-Time Executive Code for Implementation of Neural Network Backstepping Controller on ATB1000 Tank Gun Barrel 229(4)
References 233(8)
Index 241

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