Neuro-Control and its Applications

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies, ........... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advance collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Sigeru Omatu, Marzuki Khalid, and Rubiyah Yusof have pursued the new developments of fuzzy logic and neural networks to present a series volume on neuro-control methods. As they demonstrate in the opening pages of their book, there is an explosion of interest in this field. Publication and patent activity in these areas are ever growing according to international is timely. databases and hence, this volume The presentation of the material follows a complementary pattern. Reviews of existing control techniques are given along side an exposition of the theoretical constructions of fuzzy logic controllers, and controllers based on neural networks. This is an extremely useful methodology which yields rewards in the applications chapters. The series of applications includes one very thorough experimental sequence for the control of a hot-water bath.

1 Introduction.- 1.1 Introduction to Intelligent Control.- 1.2 References.- 2 Neural Networks.- 2.1 Historical Review of Neural Networks.- 2.2 Backpropagation Algorithm.- 2.2.1 Notation.- 2.2.2 Derivation of the Backpropagation Algorithm.- 2.2.3 Algorithm: Backpropagation Method.- 2.2.4 Some Discussions on the Backpropagation Algorithm.- 2.3 Conclusions.- 2.4 References.- 3 Traditional Control Schemes.- 3.1 Introduction.- 3.2 Discrete-Time PI and PID Controllers.- 3.3 Self-Tuning Control.- 3.4 Self-Tuning PI and PID Controllers.- 3.4.1 Closed Loop System.- 3.4.2 Some Interpretations Based on a Simulation Example.- 3.5 Self-Tuning PID Control — A Multivariable Approach.- 3.5.1 Simulation Example.- 3.6 Generalized Predictive Control — Some Theoretical Aspects.- 3.6.1 Cost Criterion.- 3.6.2 The Plant Model and Optimization Solution.- 3.7 Fuzzy Logic Control.- 3.7.1 Brief Overview of Fuzzy Set and Fuzzy System Theory.- 3.7.2 Basic Concept of Fuzzy Logic Controller.- 3.8 Conclusions.- 3.9 References.- 4 Neuro-Control Techniques.- 4.1 Introduction.- 4.2 Overview of Neuro-Control.- 4.2.1 Neuro-Control Approaches.- 4.2.2 General Control Configuration.- 4.3 Series Neuro-Control Scheme.- 4.4 Extensions of Series Neuro-Control Scheme.- 4.4.1 Some Discussions on On-Line Learning.- 4.4.2 Neuromorphic Control Structures.- 4.4.3 Training Configurations.- 4.4.4 Efficient On-Line Training.- 4.4.5 Training Algorithms.- 4.4.6 Evaluation of the Training.- Algorithms through Simulations.- 4.5 Parallel Control Scheme.- 4.5.1 Learning Algorithm for Parallel Control Scheme.- 4.6 Feedback Error Learning Algorithm.- 4.7 Extension of the Parallel Type Neuro-Controller.- 4.7.1 Description of Control System.- 4.7.2 Linearized Control System.- 4.7.3 Control Systems with Neural Networks.- 4.7.4 Nonlinear Observer by Neural Network.- 4.7.5 Nonlinear Controller by Neural Network.- 4.7.6 Numerical Simulations.- 4.8 Self-Tuning Neuro-Control Scheme.- 4.9 Self-Tuning PID Neuro-Controller.- 4.9.1 Derivation of the Self-Tuning PID Type Neuro-Controller.- 4.9.2 Simulation Examples.- 4.10 Emulator and Controller Neuro-Control Scheme.- 4.10.1 Off-Line Training of the Neuro-Controller and Emulator.- 4.10.2 On-Line Learning.- 4.11 Conclusions.- 4.12 References.- 5 Neuro-Control Applications.- 5.1 Introduction.- 5.2 Application of Neuro-Control to a Water-Bath Process and Comparison with Alternative Control Schemes.- 5.2.1 Introduction.- 5.2.2 Description of the Water Bath Temperature Control System.- 5.2.3 Neuro-Control Scheme.- 5.2.4 Fuzzy Logic Control Scheme.- 5.2.5 Generalized Predictive Control Scheme.- 5.2.6 Experimental Results and Discussions.- 5.2.7 Conclusions.- 5.3 Stabilizing an Inverted Pendulum by Neural Networks.- 5.3.1 Introduction.- 5.3.2 Description of the Inverted Pendulum System.- 5.3.3 Initial Start-Up Control Using Fuzzy Logic.- 5.3.4 Using Optimal Control Strategy for the Stabilization of the Inverted Pendulum.- 5.3.5 Fine Improvement by Using Neural Networks.- 5.3.6 Conclusions.- 5.4 Speed Control of an Electric Vehicle by Self-Tuning PID Neuro-Controller.- 5.4.1 Introduction.- 5.4.2 The Electric Vehicle Control System.- 5.4.3 Self-Tuning PID Type Neuro-Controller.- 5.4.4 Application to Speed Control of Electric Vehicle.- 5.4.5 Conclusions.- 5.5 MIMO Furnace Control with Neural Networks.- 5.5.1 Introduction.- 5.5.2 Description of Furnace Control System.- 5.5.3 The Neuro-Control Scheme.- 5.5.4 Experiments and Discussions.- 5.5.5 Conclusions.- 5.6 Concluding Remarks.- 5.7 References.- Program List.