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

, Pages xv-xix
1 - Adaptive inner version control

, Pages 1-22
2 - An set of rules for powerful adaptive keep watch over with much less past 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 structures through switching regulate: the tactic of localization

, Pages 80-118
6 - Adaptive nonlinear regulate: Passivation and small achieve techniques

, Pages 119-158
7 - energetic identity for regulate of discrete-time doubtful nonlinear systems

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

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

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

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

, Pages 287-307
12 - Adaptive strong regulate scheme with an software to PM synchronous motors

, Pages 308-327

, Pages 329-335

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Extra resources for Adaptive Control Systems

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That is, only assumptions (A1)--(A3) are needed. 23) where with zero initial condition. It should be noted that ~l and ~2will be always positive and non-decreasing. 1 The prediction error ~(t) is used in the modified least squares algorithm to ensure that the estimator property (iii) in the following lemma can be established. The properties of the above modified least squares parameter estimator are summarized in the following lemma. 23) has the following properties: (i) 0(t) is bounded, and ]10(t)- 0 ( t - 1)1[ E/2.

1) where up(t) and yp(t) are plant input and plant output respectively, #Pu(s) is the multiplicative unmodelled dynamics with some # E R +, and do is the output disturbance. 2) where np(s) and dp(s) are some monic coprime polynomials and kp is the high frequency gain. 1) is not precisely known but some prior knowledge about the transfer function may be available. 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.

On Automat. , Vol. AC-37, 556-565. [14] Ioannou, P. A. and Tsakalis, K. S. (1986). 'A Robust Direct Adaptive Controller,' IEEE Trans. on Automat. , Vol. AC-31, 1033-1043, Nov. 2 An algorithm for robust adaptive control with less prior knowledge G. Feng, Y. A. J iang and R. Zmood Abstract A new robust discrete-time singularity free direct adaptive control scheme is proposed with respect to a class of modelling uncertainties in this chapter. Two key features of this scheme are that a relative dead zone is used but no knowledge of the parameters of the upper bounding function on the class of modelling uncertainties is required, and no knowledge of the lower bound on the leading coefficient of the parameter vector is required to ensure the control law singularity free.

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