High-Order Parameter Tuners for the Adaptive Control of Linear and Nonlinear Systems

  • A. S. Morse
Chapter
Part of the Progress in Systems and Control Theory book series (PSCT, volume 12)

Abstract

A new method of parameter tuning is introduced which generates as outputs not only a set of tuned parameters, but also the first \(\bar n\) time derivatives of each parameter, \(\bar n\) being a prespecified positive integer. Using a modification of the idea of certainty-equivalence control, called “surrogate model control,” it is explained how to construct an “identifier-based parameterized controller” which, when tuned using the aforementioned method, is capable of adaptively stabilizing any member of a specially structured family of nonlinear systems. An analysis is made of a model reference adaptive control algorithm consisting of a simplified high-order tuner and an identifier-based parameterized controller. It is shown that this algorithm can adaptively stabilize siso processes admitting linear, minimum phase models with different relative degrees.

Keywords

Adaptive Control Adaptive Controller Minimum Phase Model Reference Adaptive Control Adaptive Stabilization 
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 Science+Business Media New York 1992

Authors and Affiliations

  • A. S. Morse
    • 1
  1. 1.Department of Electrical EngineeringYale UniversityNew HavenUSA

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