Computing-predictor-network-based exact model matching and identification for a class of nonlinear mechanical systems

Extensions of the results of model reference adaptive control to nonlinear and/or time-varying systems have rarely been achieved. For this purpose, the authors present an alternative method of exact model matching (EMM) based on a computing predictor network with assigned damping characteristics under prior knowledge of the plant parameters. A direct method for identifying physical parameters based on small parameter changes of a simulator, called the SANQ method, is introduced for overcoming the parameters' uncertainties. Several sets of simulation and experimental results are given to demonstrate the validity of the present EMM/identification method.<
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