RBF-ARX model-based nonlinear system modeling and predictive control with application to a NO x decomposition process

Abstract

This paper considers the modeling and control problem for nonstationary nonlinear systems whose dynamic characteristics depend on time-varying working-points and may be locally linearized. It is proposed to describe the system behavior by the RBF-ARX model, which is an ARX model with Gaussian radial basis function (RBF) network-style coefficients depending on the working-points of a system. The RBF-ARX model is constructed as a global model, and is estimated off-line so as to avoid the possible failure of on-line parameter estimation during real-time control. A receding horizon predictive control (RBF-ARX-MPC) strategy based on the RBF-ARX model that does not require on-line parameter estimation for the nonlinear system is presented. The local linearization of the system at each working-point may be easily obtained from the global RBF-ARX model and so the use of nonlinear programming techniques to solve the on-line optimization problem with constraints in RBF-ARX-MPC is also avoided. A fast-converging estimation method is applied to optimize the RBF-ARX model parameters. A case study and example of an industrial experiment on the nitrogen oxide (NO x ) decomposition process in thermal power plants are given to demonstrate the modeling precision and control performance.