DocumentCode :
1199162
Title :
A new approach to model reference adaptive control
Author :
Miller, Daniel E.
Author_Institution :
Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada
Volume :
48
Issue :
5
fYear :
2003
fDate :
5/1/2003 12:00:00 AM
Firstpage :
743
Lastpage :
757
Abstract :
In classical model reference adaptive control, the goal is to design a controller to make the closed-loop system act like a prespecified reference model in the face of significant plant uncertainty. Typically, the controller consists of an identifier (or tuner) which is used to adjust the parameters of a linear time-invariant (LTI) compensator, and under suitable assumptions on the plant model uncertainty it is proven that asymptotic matching is achieved. However, the controller is typically highly nonlinear, and the closed loop system can exhibit undesirable behavior, such as large transients or a large control signal, especially if the initial parameter estimates are poor. Furthermore, its ability to tolerate time-varying parameters is typically limited. Here, we propose an alternative approach. Rather than estimating the plant or compensator parameters, instead we estimate what the control signal would be if the plant parameters and plant state were known and the "ideal LTI compensator" were applied. We end up with a linear periodic controller. Our assumptions are reasonably natural extensions of the classical ones to the time-varying setting; we allow rapid parameter variations, although we add a compactness requirement. We prove that we can obtain arbitrarily good tracking, explore the benefits and limitations of the approach, and provide a simulation study.
Keywords :
closed loop systems; compensation; feedback; linear systems; model reference adaptive control systems; parameter estimation; time-varying systems; SISO systems; closed loop system; compensation; control signal; feedback; linear time-invariant system; model reference adaptive control; model uncertainty; parameter estimation; time-varying systems; Adaptive control; Closed loop systems; Control systems; Nonlinear control systems; Parameter estimation; Recursive estimation; State estimation; Tuners; Uncertainty; Upper bound;
fLanguage :
English
Journal_Title :
Automatic Control, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9286
Type :
jour
DOI :
10.1109/TAC.2003.811251
Filename :
1198596
Link To Document :
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