An RBF-ARX Model-Based Variable-Gain Feedback RMPC Algorithm

Abstract

The RBF-ARX model has been used intensively in modeling and control of nonlinear systems, in which the coefficients of the NARX model are approximated with RBF networks. In this paper, motivated by the fact that the state feedback control policy with variable-gain feedback can support more freedom for the design of RMPCs, we propose an RBF-ARX model-based variable-gain feedback RMPC synthesis method. First, a polytopic state space model construction method is designed, in which the variation rate information of the model parameters is also utilized to improve accuracy of the system model prediction. And then, a robust variable-gain feedback predictive control algorithm is designed to enlarge design freedom and improve control performance. Finally, the verification of the feasibility and effectiveness of our RMPC is conducted on a CSTR process.

Keywords

• NARX model
• robustness
• model predictive control
• variable-gain feedback