Multiple model adaptive control. Part 1: Finite controller coverings

Anderson, Brian D. O.; Brinsmead, Thomas S.; Bruyne, F. De; Hespanha, João P.; Liberzon, Daniel; Morse, A. Stephen

doi:10.1002/1099-1239(200009/10)10:11/12<909::aid-rnc532>3.0.co;2-z

Cited by 188 publications

(116 citation statements)

References 14 publications

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“…In one of the multiple-model approaches, the identification of the most likely model is carried out by a "supervisor" which switches different controllers, based primarily on deterministic concepts (Morse, 1996(Morse, , 1997(Morse, , 1998(Morse, , 2004Narendra and Balakrishnan, 1997;Anderson et al, 2000Anderson et al, , 2001Hespanha et al, 2001). These proofs and results were presented for the case of SISO systems.…”

Section: Brief Historical Perspectivementioning

confidence: 99%

“…Unfortunately, very little has been done in integrating the non-adaptive mixed-µ design methodology with that of robust adaptive control studies; even though it should be apparent that the mixed-µ design method should provide us guidance on the selection and number, N , of the models to be used in any multiple-model adaptive control scheme. Notable exceptions are (Kosut and Anderson, 1988;Anderson et al, 2000;Fekri et al, 2004a,b,c;Fekri, 2005).…”

Section: Our Design Philosophymentioning

confidence: 99%

“…During the past decade a different, more deterministic, approach to adaptive control using multiple models has been initiated (Morse, 1996(Morse, , 1997(Morse, , 1998(Morse, , 2004Anderson et al, 2000Anderson et al, , 2001Hespanha et al, 2001) and research along these lines is still in progress. The methodology is called Supervisory Switching Multiple-Model Adaptive Control (SMMAC).…”

Section: Supervisory Switching Multiple-model Adaptive Control (Smmac)mentioning

confidence: 99%

See 2 more Smart Citations

Issues on Robust Adaptive Feedback Control

Athans

Fekri

Pascoal

2005

IFAC Proceedings Volumes

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We discuss recent progress in the field of robust adaptive control with special emphasis on methodologies that use multiple-model architectures. We emphasize that the selection of the number of models, estimators and compensators in such architectures must be based on precise definition of the robust performance requirements. We illustrate some of the concepts and outstanding issues for a new methodology that blends robust nonadaptive mixed µ-synthesis and stochastic hypothesis-testing concepts leading to the so-called RMMAC architecture. A numerical example is used to illustrate the RMMAC design methodology.

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Section: Brief Historical Perspectivementioning

confidence: 99%

Section: Our Design Philosophymentioning

confidence: 99%

Section: Supervisory Switching Multiple-model Adaptive Control (Smmac)mentioning

confidence: 99%

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Issues on Robust Adaptive Feedback Control

Athans

Fekri

Pascoal

2005

IFAC Proceedings Volumes

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“…This problem is addressed in [33] for linear plants. In outline, one sequentially picks P 1 , P 2 , ...P N by a systematic procedure.…”

Section: Iterative Control and Identificationmentioning

confidence: 99%

Failures of adaptive control theory and their resolution

Anderson

2005

Communications in Information and Systems

162

102

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Abstract. Adaptive control is a very appealing technology, at least in principle. Yet its use has been conditioned by an attitude of distrustfulness on the part of some practitioners. In this paper, we explain why such distrustfulness is warranted, by reviewing a number of adaptive control approaches which have proved deficient for some reason that has not been immediately apparent. The explanation of the deficiencies, which normally were reflected in unexpected instabilities, is our main concern. Such explanations, coupled with remedies for avoiding the deficiencies, are necessary to engender confidence in the technology. These include the unpredictable failure of the MIT rule; the bursting phenomenon, and how to prevent it; the Rohrs' counterexample, which attempted to disqualify all adaptive control algorithms; the notion that identification of a plant is only valid conceptually for a restricted range of controllers (with the implication that in adaptive control, certain controller changes suggested by adaptive control algorithms may introduce instability); and the concept of multiple model adaptive control.

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“…Implementation and analysis of the switching control scheme are typically simplified by considering a finite set of candidate controllers with the characteristic that, associated to each admissible process model, there is a candidate controller that ensures stability when placed in closed-loop with it. This set is called a finite controller cover ( [8,9]). In a standard switching control scheme, the compromise between robustness and performance is made when one designs the controller cover and the map associating each process model with a particular controller.…”

Section: Introductionmentioning

confidence: 99%

Cautious hierarchical switching control of stochastic linear systems

Campi

Hespanha

Prandini

2004

Adaptive Control & Signal

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Standard switching control methods are based on the certainty equivalence philosophy in that, at each switching time, the supervisor selects the candidate controller that is better tuned to the currently estimated process model. If the estimated model does not appropriately describe the process, this procedure may result in the selection of a controller that is not appropriate for the actual process.In this paper, we propose a supervisory switching logic that takes into account the uncertainty on the process description when performing the controller selection. Specifically, a probability measure describing the likelihood of the different models is computed on-line based on the collected data and, at each switching time, the supervisor selects the candidate controller that, according to this probability measure, performs the best on the average. If the candidate controller set is hierarchically structured, the supervisor automatically selects the controller that appropriately compromises robustness and performance, given the actual level of uncertainty on the process description. The use of randomized algorithms makes the supervisor implementation computationally tractable.

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Multiple model adaptive control. Part 1: Finite controller coverings

Cited by 188 publications

References 14 publications

Issues on Robust Adaptive Feedback Control

Issues on Robust Adaptive Feedback Control

Failures of adaptive control theory and their resolution

Cautious hierarchical switching control of stochastic linear systems

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