RBF-ARX MODEL-BASED ROBUST MPC FOR NONLINEAR SYSTEMS

摘要

An integrated modeling and robust model predictive control (MPC) approach is proposed for a class of nonlinear systems. First, the nonlinear system is identified off-line by a RBF-ARX model possessing linear ARX model structure and state-dependent Gaussian RBF neural network type coefficients. On the basis of the RBF-ARX model, a combination of a local linearization model and a polytopic uncertain linear parameter-varying (LPV) model are built to approximate the present and the future system’s nonlinear behavior respectively. Subsequently, based on the approximate models, a min-max robust MPC algorithm with input constraint is designed for the nonlinear systems. The closed loop stability of the MPC strategy is guaranteed by the use of parameter-dependent Lyapunov function and the feasibility of the linear matrix inequalities (LMIs). Simulation study to a Nox decomposition process illustrates the effectiveness of the modeling and robust MPC approaches proposed in this paper.