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.- 2.3 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.- 3.5 Self-Tuning PID Control — A Multivariable Approach.- 3.6 Generalized Predictive Control — Some Theoretical Aspects.- 3.7 Fuzzy Logic Control.- 3.8 Conclusions.- 3.9 References.- 4 Neuro-Control Techniques.- 4.1 Introduction.- 4.2 Overview of Neuro-Control.- 4.3 Series Neuro-Control Scheme.- 4.4 Extensions of Series Neuro-Control Scheme.- 4.5 Parallel Control Scheme.- 4.6 Feedback Error Learning Algorithm.- 4.7 Extension of the Parallel Type Neuro-Controller.- 4.8 Self-Tuning Neuro-Control Scheme.- 4.9 Self-Tuning PID Neuro-Controller.- 4.10 Emulator and Controller Neuro-Control Scheme.- 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.- 5.3 Stabilizing an Inverted Pendulum by Neural Networks.- 5.4 Speed Control of an Electric Vehicle by Self-Tuning PID Neuro-Controller.- 5.5 MIMO Furnace Control with Neural Networks.- 5.6 Concluding Remarks.- 5.7 References.- Program List.