Abstract

A robust control problem for discrete-time uncertain stochastic systems is discussed via gain-scheduled control scheme subject toH∞attenuation performance. Applying Linear Parameter Varying (LPV) modeling approach and stochastic difference equation, the uncertain stochastic systems can be described by combining time-varying weighting function and linear systems with multiplicative noise terms. Due to the consideration of stochastic behavior, the stability in the sense of mean square is applied for the system. Furthermore, two kinds of Lyapunov functions are employed to derive their corresponding sufficient conditions to solve the stabilization problems of this paper. In order to use convex optimization algorithm, the derived conditions are converted into Linear Matrix Inequality (LMI) form. Via solving those conditions, the gain-scheduled controller can be established such that the robust asymptotical stability andH∞performance of the disturbed uncertain stochastic system can be achieved in the sense of mean square. Finally, two numerical examples are applied to demonstrate the effectiveness and applicability of the proposed design method.

Highlights

  • In control problems, accurate parameters of dynamic system are always important premised assumption

  • On the other hand, based on Linear Parameter Varying (LPV) modeling approach [4,5,6], the uncertain systems can be interpreted by combining several subsystems and chosen time-varying weighting function

  • Lyapunov stability theory has been widely applied for stability analysis and synthesis of LPV systems

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Summary

Introduction

Accurate parameters of dynamic system are always important premised assumption. The accurate parameters are hardly to be obtained in practical applications due to modeling errors and natural perturbations. For this reason, robust control schemes [1,2,3,4,5,6,7,8,9,10,11,12] were proposed to guarantee stability of dynamic system with admissible uncertainties. On the other hand, based on LPV modeling approach [4,5,6], the uncertain systems can be interpreted by combining several subsystems and chosen time-varying weighting function.

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