BP neural network-based explicit MPC of nonlinear boiler-turbine systems

Abstract

This paper proposes a new explicit model predictive control (EMPC) scheme of constrained nonlinear systems with unknown but bounded input disturbances. Firstly, support vector machine is used to learn internal and external approximations of the feasible state space of the EMPC. Then, the control surface on the feasibility of EMPC is constructed by a backpropagation neural network (BPNN). The finite horizon optimal control solution to the EMPC can be computed from real-time data by training the control surface. The proposed EMPC is also suitable for nonlinear systems with higher dimensions in terms of reducing online computational burdens and enhancing control accuracy. Next, the Hoeffding's Inequality is used to ensure that the EMPC law computed by the BPNN approximation complies with the specified range with a high level of confidence. Moreover, some conditions are obtained to guarantee the stability and recursive feasibility of the EMPC with probabilistic assurances. Finally, a 160 MW boiler-turbine system is employed to verify the effectiveness and applications of the proposed method.