A RBF-ARX model-based robust MPC for tracking control without steady state knowledge. (March 2017)

• Record Type: Journal Article
• Authors: Zhou, Feng; Peng, Hui; Qin, Yemei; Zeng, Xiaoyong; Tian, Xiaoying; Xu, Wenquan
• Is Part Of: Journal of process control. Volume 51(2017)
• Page Start: 42
• Page End: 54
• Publication Date: 2017-03
• Subjects: Model predictive control -- Radial basis function networks -- Robustness -- CSTR process -- Two tank system; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2016.12.008
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A quasi-min–max robust MPC is designed based on the structural characteristic of RBF-ARX model. RBF-ARX-RMPC is designed for system output-tracking control without steady state knowledge. The comparative experiments demonstrated the effectiveness of the RMPC on two simulators. Abstract: A RBF-ARX modeling and robust model predictive control (MPC) approach to achieving output-tracking control of the nonlinear system with unknown steady-state knowledge is proposed. On the basis of the RBF-ARX model with considering the system time delay, a local linearization state-space model is obtained to represent the current behavior of the nonlinear system, and a polytopic uncertain linear parameter varying (LPV) state-space model is built to represent the future system's nonlinear behavior. Based on the two models, a quasi-min–max MPC algorithm with constraint is designed for output-tracking control of the nonlinear system with unknown steady state knowledge. The optimization problem of the quasi-min–max MPC algorithm is finally converted to the convex linear matrix inequalities (LMIs) optimization problem. Closed-loop stability of the MPC strategy is guaranteed by the use of parameter-dependent Lyapunov function and feasibility of the LMIs. Two examples, i.e. the modeling and control of a continuously stirred tank reactor (CSTR) and a two tank system demonstrate the effectiveness of the RBF-ARX modeling and robust MPC approach.