Robust Fuzzy Model Predictive Control of Discrete-Time Takagi–Sugeno Systems With Nonlinear Local Models

Abstract

Robust fuzzy model predictive control of discrete nonlinear systems is investigated in this paper. A recently developed Takagi–Sugeno (T–S) fuzzy approach which uses nonlinear local models is adopted to approximate the nonlinear systems. A critical issue that restricts the practical application of classical model predictive control is the online computational cost. For model predictive control of T–S fuzzy systems, the online computational burden is even worse. Especially for complex systems with severe nonlinearities, parametric uncertainties, and disturbances, existing model predictive control of T–S fuzzy systems usually leads to a very conservative solution or even no solution in some occasions. However, more relaxed results can be achieved by the proposed fuzzy model predictive control approach which adopts T–S systems with nonlinear local models. Another advantage is that online computational cost of the optimization problem through solving matrix inequalities can be significantly reduced at the same time. Simulations on a numerical example and a two-tank system are presented to verify the effectiveness and advantages of the proposed method. Comparisons among several T–S fuzzy approaches are illustrated and show that the best settling time is achieved via the proposed method.