Iterative Solution of Linear Matrix Inequalities for the Combined Control and Observer Design of Systems with Polytopic Parameter Uncertainty and Stochastic Noise

Algorithms

Swantje Romig¹  Andreas Rauh²  Robert Dehnert³  Sabine Lerch³  Bernd Tibken³ 

[1] Chair of Turbomachinery, University of Rostock, D-18059 Rostock, Germany;ENSTA Bretagne, Lab-STICC, 29806 Brest, France;School of Electrical, Information and Media Engineering, Automatic Control, University of Wuppertal, D-42119 Wuppertal, Germany;

关键词: polytopic uncertainty;    stochastic disturbances;    robust control;    observer design;    linear matrix inequalities;    optimization;   

DOI  :  10.3390/a14070205

来源: DOAJ

【 摘 要 】

Most research activities that utilize linear matrix inequality (LMI) techniques are based on the assumption that the separation principle of control and observer synthesis holds. This principle states that the combination of separately designed linear state feedback controllers and linear state observers, which are independently proven to be stable, results in overall stable system dynamics. However, even for linear systems, this property does not necessarily hold if polytopic parameter uncertainty and stochastic noise influence the system’s state and output equations. In this case, the control and observer design needs to be performed simultaneously to guarantee stabilization. However, the loss of the validity of the separation principle leads to nonlinear matrix inequalities instead of LMIs. For those nonlinear inequalities, the current paper proposes an iterative LMI solution procedure. If this algorithm produces a feasible solution, the resulting controller and observer gains ensure robust stability of the closed-loop control system for all possible parameter values. In addition, the proposed optimization criterion leads to a minimization of the sensitivity to stochastic noise so that the actual state trajectories converge as closely as possible to the desired operating point. The efficiency of the proposed solution approach is demonstrated by stabilizing the Zeeman catastrophe machine along the unstable branch of its bifurcation diagram. Additionally, an observer-based tracking control task is embedded into an iterative learning-type control framework.

【 授权许可】

Unknown