Robust fuzzy model predictive control of discrete-time Takagi–Sugeno systems with nonlinear local models

• Main Authors: Teng, Long, Wang, Youyi, Cai, Wenjian, Li, Hua
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Electrical and electronic engineering; Input-to-state Stability; Model Predictive Control (MPC)
• Online Access: https://hdl.handle.net/10356/142450
• Institution: Nanyang Technological University

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.