Model Reference Adaptive Control
Part of the Advances in Industrial Control book series (AIC)
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.
KeywordsProbability 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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© Springer-Verlag London 2000