A RBF-ARX model-based robust MPC for tracking control without steady state knowledge

作者：Zhou, Feng; Peng, Hui^*; Qin, Yemei; Zeng, Xiaoyong; Tian, Xiaoying; Xu, Wenquan

来源：Journal of Process Control, 2017, 51: 42-54.

DOI：10.1016/j.jprocont.2016.12.008

• Model predictive control
• Radial basis function networks
• Robustness
• CSTR process
• Two tank system

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.

• 出版日期2017-3
• 单位湖南商学院; 中南大学