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Multiple-model adaptive control using set-valued observers
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DOI：10.1002/rnc.3005.png)
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A multiple-model adaptive control methodology is proposed that is able to provide stability and performance guarantees, for uncertain linear parameter-varying plants. The identification problem is addressed by taking advantage of recent advances in model falsification using set-valued observers (SVOs). These SVOs provide set-valued estimates of the state of the system, according to its dynamic model. If such estimate is the empty set, the underlying dynamic model is invalidated, and a different controller is connected to the loop. The behavior of the proposed control algorithm is demonstrated in simulation, by resorting to a mass-spring-dashpot system. As a caveat, the computational burden associated with the SVOs can be prohibitive under some circumstances. Copyright (c) 2013 John Wiley & Sons, Ltd.
Keyword:
adaptive control
multiple-model
set-valued observers
uncertain systems
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