A RBF-ARX model-based robust MPC for tracking control without steady state knowledge. (March 2017) - Zhou, Feng; Peng, Hui; Qin, Yemei; Zeng, Xiaoyong; Tian, Xiaoying; Xu, Wenquan
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.