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Adaptive Control with Recurrent High-order Neural Networks Theory and Industrial Applications

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer met...

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Bibliographic Details
Main Authors: Rovithakis, George A., Christodoulou, Manolis A. (Author)
Format: eBook
Language:English
Published: London Springer London 2000, 2000
Edition:1st ed. 2000
Series:Advances in Industrial Control
Subjects:
Control, Robotics, Automation
Applied Dynamical Systems
Computer Simulation
Artificial Intelligence
Computer Modelling
Nonlinear Theories
Control Engineering
Robotics
Automation
Dynamics
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 1. Introduction
  • 1.1 General Overview
  • 1.2 Book Goals & Outline
  • 1.3 Notation
  • 2. Identification of Dynamical Systems Using Recurrent High-order Neural Networks
  • 2.1 The RHONN Model
  • 2.2 Learning Algorithms
  • 2.3 Robust Learning Algorithms
  • 2.4 Simulation Results
  • Summary
  • 3. Indirect Adaptive Control
  • 3.1 Identification
  • 3.2 Indirect Control
  • 3.3 Test Case: Speed Control of DC Motors
  • Summary
  • 4. Direct Adaptive Control
  • 4.1 Adaptive Regulation — Complete Matching
  • 4.2 Robustness Analysis
  • 4.3 Modeling Errors with Unknown Coefficients
  • 4.4 Tracking Problems
  • 4.5 Extension to General Affine Systems
  • Summary
  • 5. Manufacturing Systems Scheduling
  • 5.1 Problem Formulation
  • 5.2 Continuous-time Control Law
  • 5.3 Real-time Scheduling
  • 5.4 Simulation Results
  • Summary
  • 6. Scheduling using RHONNs: A Test Case
  • 6.1 Test Case Description
  • 6.2 Results & Comparisons
  • Summary
  • References

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