Robust fuzzy model predictive control for nonlinear discrete-time systems

Abstract

This paper studies the issue of Robust Fuzzy Model Predictive Control (RFMPC) of nonlinear discrete-time systems in the presence of disturbance. To this end, Takagi-Sugeno (T-S) fuzzy model is adopted in order to characterize uncertain nonlinear systems and facilitate providing controller for such systems. Non-parallel distributed compensation (non-PDC) technique is applied to design improved RFPMC with better performance. The optimization problem of RFMPC is represented in terms of linear matrix inequalities (LMIs) so as to decrease online computational burden. Besides, feasibility and stability which are decisive factors in MPC theory are investigated for the proposed method. Ultimately, two examples including cart-damper-spring system are simulated to evaluate the results of this research work.