Efficient Fuzzy-MPC for Nonlinear systems : rule rejection

In this paper, efficient fuzzy model predictive control (EEMPC) is studied for nonlinear systems. Nonlinear system is represented by a Takagi-Sugeno (T-S) fuzzy system that is useful for nonlinear plant modeling. T-S fuzzy system consists of several linear models. The control problem is solved based on MPC which solves an online optimization problem with linear matrix inequality (LMI) at each step to compute an optimal control. This also increases the online computational complexity. The proposed efficient fuzzy MPC using rule rejection reduces such online computational complexity. It is possible to diminish a number of LMIs by rejecting less weighting rules in T-S fuzzy system at each time. To illustrate its effectiveness, the proposed method is applied to the inverted pendulum on a cart with nonlinear characteristics