RBF-ARX model-based MPC strategies with application to a water tank system. (October 2015)

• Record Type: Journal Article
• Authors: Zhou, Feng; Peng, Hui; Qin, Yemei; Zeng, Xiaoyong; Xie, Wenbiao; Wu, Jun
• Is Part Of: Journal of process control. Volume 34(2015:Oct.)
• Page Start: 97
• Page End: 116
• Publication Date: 2015-10
• Subjects: RBF-ARX model -- Model-based predictive control -- Real-time control -- Liquid level control -- Water tank system; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2015.07.010
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Three MPC strategies are proposed based on the structural characteristic of RBF-ARX model. The MPC-GNO is designed on the basis of the globally nonlinear feature of RBF-ARX model. Real-time control performances of the three MPC strategies applied to a water tank system are compared. Computational complexity of the three types of MPC strategy is discussed. Abstract: A hybrid pseudo-linear RBF-ARX model that combines Gaussian radial basis function (RBF) networks and linear ARX model structure is utilized for representing the dynamic behavior of a class of smooth nonlinear and non-stationary systems. This model is locally linear at each working point and globally nonlinear within whole working range. Based on the structural characteristics of the RBF-ARX model, three receding horizon predictive control (RBF-ARX-MPC) strategies are designed: (1) the RBF-ARX-MPC algorithm based on single-point linearization (MPC-SPL); (2) the RBF-ARX-MPC algorithm based on multi-point linearization (MPC-MPL); and (3) the RBF-ARX-MPC algorithm based on globally nonlinear optimization (MPC-GNO). In the MPC-SPL, the future multi-step-ahead predictive output of the system is obtained based on the local linearization of the RBF-ARX model at only current working-point, while in the MPC-MPL the future long-term output prediction is obtained according to the future local characteristics from previous online optimization results of the RBF-ARX model based MPC. In the MPC-GNO, the globally nonlinear characteristics of the RBF-ARX model are fully used for online getting control variables of the MPC. Real-time control experiments for the three type MPCs are carried out on a water tank system, which are also compared with a classical PID control and a traditional linear ARX model-based MPC. The results verify that the modeling method and the model-based predictive control strategies are realizable and effective for the nonlinear and unstable system. Moreover, it is also shown that the MPC-GNO can obtain better control performance but need more computation time compared to the other MPCs, which makes it possible to be applied into some slowly varying processes.