Neuro-Control and its Applications

Overview

This study provides a hands-on approach to intelligent control with a special focus on neuro-control. The authors present various types of neuro-control paradigms based on the backpropagation algorithm. Examples for applications of neuro-control techniques to real control problems illustrate how the techniques are implemented. A comparison of neuro-control methodologies with conventional and traditional control methods enables the reader to select the appropriate control technique. Neuro-Control and Its ...
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Overview

This study provides a hands-on approach to intelligent control with a special focus on neuro-control. The authors present various types of neuro-control paradigms based on the backpropagation algorithm. Examples for applications of neuro-control techniques to real control problems illustrate how the techniques are implemented. A comparison of neuro-control methodologies with conventional and traditional control methods enables the reader to select the appropriate control technique. Neuro-Control and Its Applications is a response to the explosion of interest in neuro-control methods. It is predicted that neuro-control techniques will soon be available for domestic appliances and industrial control systems, similar to the fuzzy logic revolution of recent years.
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Product Details

  • ISBN-13: 9781447130604
  • Publisher: Springer London
  • Publication date: 12/13/2011
  • Series: Advances in Industrial Control Series
  • Edition description: Softcover reprint of the original 1st ed. 1996
  • Edition number: 1
  • Pages: 255
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.57 (d)

Table of Contents

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.
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