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The authors propose a discrete-time model-reference adaptive control algorithm which is robust with respect to stable additive and multiplicative plant uncertainties. The algorithm uses the same controller structure as the one that would be used if the parameters of the dominant part of the plant were known. Using essentially the same assumptions that are necessary to design a fixed controller based on the exact knowledge of the dominant part of the plant, it is shown that the proposed algorithm guarantees boundedness for all signals in the closed loop and 'small' residual tracking errors for any bounded initial conditions. In the absence of plant uncertainties, the algorithm guarantees zero residual tracking errors. 838 843 Sponsors: IEEE Control Systems Soc, New York, NY, USA SIAM, Philadelphia, PA, USA Operations Research Soc of America, Baltimore, MD, USA Greek Ministry of Culture, Athens, Greece Natl Technical Univ of Athens, Athens, Greece et al Conference code: 9775 Cited By :10

The authors have studied the problem of developing an adaptive control law that can force the output of a linear time-varying plant to track the output of a linear time-varying reference model and at the same time guarantee the internal stability of the closed-loop plant. Use is made of the authors' recently proposed (1987) model reference controller structure, which guarantees stability and zero tracking error for a general class of linear time-varying plants with known parameters. An adaptive law is designed to estimate the controller parameters. It is shown that the assumption of slow parameter variations in the adaptive case can be relaxed if some information about the frequency or the form of the fast-varying parameters is available a priori. Such information can be incorporated in the adaptive law so that stability and tracking performance is considerably improved for a wide class of linear time-varying plants which includes plants with fast-varying parameters. 354 359 Sponsors: IEEE, Control Systems Soc, New York, NY, USA SIAM, Philadelphia, PA, USA Operations Research Soc of America, Baltimore, MD, USA Conference code: 11384 Cited By :1