Research on Model-driven Adaptive Control Systems based on the Error of Closed-loop Parameters

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The purpose of the research is to formulate a new method of adaptive control called "Model-driven control" from a control theoretical point of view and to establish a theory for. analysis and synthesis as powerful control design framework. We have revealed significances and its properties of model-driven control architecture from theoretical and experimental aspects. A fundamental concept of model-driven control comes from a motor control of human. In this research, in order to provide new developments of method for dealing with "Complicated System" for both fields of adaptive control and motor control we tried the following three themes mainly (1) The formulation of model-driven adaptive control and the relation to motor control, (2) The establishment of identification method for complicated systems and the relation to learning control and (3) Control theoretical approaches to various nonlinear complicated systemsIn (1), we formulated a well-known "Feedback Error Learning method" as two-degrees of freedom control systems and investigated its closed-loop structure. A stability of the closed-loop system including adaptation and a convergence of its adaptation parameters were shown theoretically. Furthermore, these results were extended to a case where a time delay exists in the feedback loop. Each, result in (2) includes interest approaches to the essential difficulties for dealing with complicated systems. In (3), we obtained various outcomes which related to various complicated system, e.g., nonlinear robot arm, a hear exchanger in a fuel cell, control biology, biomechanics of human motor control, quantum control systems, a large scale network system and so on. In particular, results on control biology and quantum control system moved forward the frontier of new field in control theory.