Fuzzy Model Predictive Control With Enhanced Robustness for Nonlinear System via a Discrete Disturbance Observer

This paper addresses the tracking accuracy and robustness enhancement problems of fuzzy model based predictive control (MPC) for a class of nonlinear systems subjecting to lumped disturbances composed of bounded unknown disturbances and a model-plant mismatch. Main features of the proposed method are: 1) A fuzzy disturbance observer and an auxiliary controller are jointly developed to meet a certain control objective that minimizes the peak bound of the errors caused by the lumped disturbances, which eventually leads to desired offset-free tracking performance. 2) A pre-computed robust positively invariant set whose central is the nominal state is derived with the premise of input-to-state stability. 3) Tightened constraints for the guarantee of recursive feasibility of MPC is computed off-line and the quasi-min-max fuzzy MPC is elaborately designed according to a piecewise Lyapunov function. Furthermore, characteristics of robustness enhancement and low on-line computational burden are obtained as compared with the existing offset-free MPCs, and further the impacts of estimation error arising from sampling time and admissible target set on the system performance are also discussed. Two simulation examples verify the effectiveness of the proposed approach ensuring the satisfaction of constraints.

Abstract licence: CC BY-NC-ND

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