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Bounded Dynamic Stochastic Systems Modelling and Control

Over the past decades, although stochastic system control has been studied intensively within the field of control engineering, all the modelling and control strategies developed so far have concentrated on the performance of one or two output properties of the system, such as minimum-variance contr...

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Bibliographic Details
Main Author: Wang, Hong
Format: eBook
Language:English
Published: London Springer London 2000, 2000
Edition:1st ed. 2000
Series:Advances in Industrial Control
Subjects:
Control, Robotics, Automation
Control And Systems Theory
Probability Theory
Chemistry, Technical
Control Engineering
Robotics
Automation
Industrial Chemistry
Probabilities
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 1 Preliminaries
  • 1.1 Introduction
  • 1.2 An example: flocculation model
  • 1.3 The aim of the new development
  • 1.4 The structure of the book
  • 1.5 Random variables and stochastic processes
  • 1.6 Stochastic processes
  • 1.7 Some typical distributions
  • 1.8 Conclusions
  • 2 Control of SISO Stochastic Systems: A Fundamental Control Law
  • 2.1 Introduction
  • 2.2 Preliminaries on B-splines artificial neural networks
  • 2.3 Model representation
  • 2.4 System modelling and parameter estimation
  • 2.5 Control algorithm design
  • 2.6 Discussions
  • 2.7 Examples
  • 2.8 Conclusions
  • 3 Control of MIMO Stochastic Systems: Robustness and Stability
  • 3.1 Introductionx
  • 3.2 Model representation
  • 3.3 The controller using V(k)
  • 3.4 The controller using f(y, U(k))
  • 3.5 An illustrative example
  • 3.6 Conclusions and discussions
  • 4 Realization of Perfect Tracking
  • 4.1 Introduction
  • 4.2 Preliminaries and model representation
  • 4.3 Main result
  • 4.4 Simulation results
  • 8.6 An identification based FDD
  • 8.7 Fault diagnosis
  • 8.8 Discussions and conclusions
  • 9 Advanced Topics
  • 9.1 Introduction
  • 9.2 Square root models
  • 9.3 Control algorithm design
  • 9.4 Simulations
  • 9.5 Continuous-time models
  • 9.6 The control algorithm
  • 9.7 Control of the mean and variance
  • 9.8 Singular stochastic systems
  • 9.9 Pseudo ARMAX systems
  • 9.10 Filtering issues
  • 9.11 Conclusions
  • References
  • 4.5 An LQR based algorithm
  • 4.6 Conclusions
  • 5 Stable Adaptive Control of Stochastic Distributions
  • 5.1 Introduction
  • 5.2 Model representation
  • 5.3 On-line estimation and its convergence
  • 5.4 Adaptive control algorithm design
  • 5.5 Stability analysis
  • 5.6 A simulated example
  • 5.7 Conclusions
  • 6 Model Reference Adaptive Control
  • 6.1 Introduction
  • 6.2 Model representation
  • 6.3 An adaptive controller design
  • 6.4 Adaptive tuning rules for K(t) and Q(t)
  • 6.5 Robust adaptive control scheme
  • 6.6 A case study
  • 6.7 Conclusions and discussions
  • 7 Control of Nonlinear Stochastic Systems
  • 7.1 Introduction
  • 7.2 Model representation
  • 7.3 Control algorithm design
  • 7.4 Stability issues
  • 7.5 A neural network approach
  • 7.6 Two examples
  • 7.7 Calculation of ?
  • 7.8 Conclusions
  • 8 Application to Fault Detection
  • 8.1Introduction
  • 8.2 Model representation
  • 8.3 Fault detection
  • 8.4 An adaptive diagnostic observer
  • 8.5 Discussions

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