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Model Reference Adaptive Control

  • Hong Wang
Part of the Advances in Industrial Control book series (AIC)

Abstract

Control algorithms discussed in Chapters 2–5 are generally based on the discrete-time model and in most cases the input and output models are used to formulate the algorithm. In this chapter, an alternative approach will be described using the state space model. We will only consider unknown continuous-time linear systems, where the model reference adaptive control algorithm ([20, 21, 19, 25]) will be applied to design stable adaptive control input which
  • stablizes the closed loop system, and

  • realises the perfect tracking of the output probability density function with respect to the given distribution.

Keywords

Probability Density Function Control Input Adaptive Control Close Loop System Error Dynamic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2000

Authors and Affiliations

  • Hong Wang
    • 1
  1. 1.Department of Paper ScienceUMISTManchesterUK

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