一类混合不确定系统的对偶自适应控制

doi:10.3969/j.issn.0255-8297.2018.06.014

Abstract

Abstract: For the linear quadratic Gaussian (LQG) problems with unknown constant parameters and accurately measurable state, the separation theorem is no longer valid due to the coupling between parameters estimation and control gain, which lead to the failure to the analytical solution of the control law. The dual adaptive control method is proposed in this paper, where a state space model of parameters estimation is established, control gain is obtained by rolling dynamic programming, Kalman flter is used to estimate unknown parameters, the present difculties about the mutual coupling between estimated gain and control gain are overcome, and the controller with suboptimal characteristics is designed. On the one hand, the controller can optimize the control target, on the other hand, it can also learn unknown parameters effectively. The simulation results show the effectiveness of the proposed control algorithm.

Key words: dual adaptive control, linear quadratic Gaussian (LQG) problem, rolling dynamic programming, Kalman flter

CLC Number:

TP273

SHANG Ting, QIAN Fu-cai, LIU Lei, HU Shao-lin. Dual Adaptive Control for a Class of Mixed Uncertainty Systems[J]. Journal of Applied Sciences, 2018, 36(6): 1022-1030.

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Dual Adaptive Control for a Class of Mixed Uncertainty Systems

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