Adaptive Control Systems by Gang Feng and Rogelio Lozano (Auth.)

By Gang Feng and Rogelio Lozano (Auth.)

Content material:
List of contributors

, Pages ix-xiii
Preface

, Pages xv-xix
1 - Adaptive inner version control

, Pages 1-22
2 - An set of rules for powerful adaptive keep an eye on with much less previous knowledge

, Pages 23-40
3 - Adaptive variable constitution control

, Pages 41-62
4 - oblique adaptive periodic control

, Pages 63-79
5 - Adaptive stabilization of doubtful discrete-time platforms through switching keep an eye on: the strategy of localization

, Pages 80-118
6 - Adaptive nonlinear keep watch over: Passivation and small achieve techniques

, Pages 119-158
7 - lively id for regulate of discrete-time doubtful nonlinear systems

, Pages 159-183
8 - optimum adaptive monitoring for nonlinear systems

, Pages 184-214
9 - good adaptive platforms within the presence of nonlinear parametrization

, Pages 215-259
10 - Adaptive inverse for actuator compensation

, Pages 260-286
11 - good multi-input multi-output adaptive fuzzy/neural control

, Pages 287-307
12 - Adaptive powerful keep watch over scheme with an software to PM synchronous motors

, Pages 308-327
Index

, Pages 329-335

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Extra resources for Adaptive Control Systems

Sample text

Automat. , Vol. AC-30, 881-889. [2] Egardt, B. (1979). Stability of Adaptive Controllers, Lecture Notes in Control and Information Sciences, New York, Springer Verlag. [3] Ortega, R. and Tang, Y. (1989). 'Robustness of Adaptive Controllers - A Survey', Automatica, Vol. 25, 651-677. [4] Ydstie, B. E. (1989). 'Stability of Discrete MRAC-revisited', Systems and Control Letters, Vol. 13, 429-438. Adaptive Control Systems 39 [5] Naik, S. , Kumar, P. , Ydstie, B. E. (1992). 'Robust Continuous-time Adaptive Control by Parameter Projection', IEEE Trans.

2, we can guarantee the exponential convergent behaviour and finite-time tracking performance of all the auxiliary errors ea~. a2+ " " + ~ap, it can be concluded that Ea converges to zero exponentially and e0 converges to a small residual set whose size depends on the design parameter r. 2. 1.

3) where M(s) is a stable transfer function and rm(t) is a uniformly bounded reference input. In order to achieve such an objective, we need some assumptions on the modelled part of the plant and the reference model as well as the unmodelled part of the plant. These assumptions are made in the following. For the modelled part of the plant and reference model: (A1) All the coefficients of np(s) and dp(s) are unknown a priori, but the order of P(s) and its relative degree are known to be n and p, respectively.

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