The emphasis of this thesis is on developing a theoretical framework for the practical design of adaptive systems. The state of the art in industrial adaptive control is to combine a parameter estimator with an (existing) linear control-loop. The success of the approach depends upon the knowledge of the physical parameters after completion of the estimation process. In the presence of disturbances, though, a correct estimation of the parameters is impossible. It is first shown how a system with unknown parameters can be controlled adaptively, without relying on parameter convergence. Following this, three classes of disturbances and the corresponding methods to reject them are developed: external disturbances, unmodelled dynamics and time-variations. In the latter case, a new algorithm based on multiple adaptive models is presented which was developed at Yale University during regular visits of the author at the Center for Systems Science. An experimental study of an adaptively controlled two-mass system concludes the thesis.
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The emphasis of this thesis is on developing a theoretical framework for the practical design of adaptive systems. The state of the art in industrial adaptive control is to combine a parameter estimator with an (existing) linear control-loop. The success of the approach depends upon the knowledge of the physical parameters after completion of the estimation process. In the presence of disturbances, though, a correct estimation of the parameters is impossible. It is first shown how a system with...
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