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In this paper, we develop an output feedback adaptive control framework for continuous-time nonminimum phase multivariable systems for output stabilization and command following. The approach is based on a nonminimal state space realization that generates an expanded set of states using the filtered inputs and filtered outputs of the original system. Specifically, a direct adaptive controller for...
A direct adaptive control method with a simple identification of the controller parameters is presented for a class of uncertain time-varying nonlinear systems. The systems are divided into a finite number of subsystems, which are interpolated by functions of an exogenous scheduling variable. The adaptive method yields stability of all signals in the closed-loop, as well as convergence of the state...
This study focuses on the development of an adaptive fuzzy-neural-network control (AFNNC) scheme for an n-link robot manipulator to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order...
This paper is concerned with the control of a class of continuous-time nonlinear dynamic systems preceded by unknown non-symmetrical, non-equal slope dead-zones. By exploring the properties of the dead-zone model intuitively and mathematically, an adaptive sliding mode inverse control scheme is developed by constructing a dead-zone inverse for compensation. The proposed approach ensures global stability...
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