A new T-S fuzzy model predictive control for nonlinear processes

作者：

Highlights：

• A new Takagi-Sugeno system based Kernel ridge regression (TS-KRR) was proposed.

• The TS-KRR strategy is implemented for both adaptive and offline identification.

• The TS-KRR was integrated with the GPC to control discrete-time nonlinear systems.

• The proposed controller showed good results in TS fuzzy GPC with offline modeling.

• The adaptive TS fuzzy GPC showed good results in dealing with disturbances.

摘要

•A new Takagi-Sugeno system based Kernel ridge regression (TS-KRR) was proposed.•The TS-KRR strategy is implemented for both adaptive and offline identification.•The TS-KRR was integrated with the GPC to control discrete-time nonlinear systems.•The proposed controller showed good results in TS fuzzy GPC with offline modeling.•The adaptive TS fuzzy GPC showed good results in dealing with disturbances.

论文关键词：Generalized Predictive Control,Takagi-Sugeno fuzzy system,Kernel ridge regression,Clustering algorithm,Particle Swarm Optimization,Takagi-Sugeno system based Kernel ridge regression,Sliding-window Kernel Recursive Least squares

论文评审过程：Received 11 January 2017, Revised 11 June 2017, Accepted 26 June 2017, Available online 28 June 2017, Version of Record 5 July 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.06.039