Robust Model Predictive Controller Using Recurrent Neural Networks for Input–Output Linear Parameter Varying Systems

Abstract

This paper develops a model predictive controller (MPC) for constrained nonlinear MIMO systems subjected to bounded disturbances. A linear parameter varying (LPV) model assists MPC in dealing with nonlinear dynamics. In this study, the nonlinear process is represented by an LPV using past input–output information (LPV-IO). Two primary objectives of this study are to reduce online computational load compared with the existing literature of MPC with an LPV-IO model and to confirm the robustness of the controller in the presence of disturbance. For the first goal, a recurrent neural network (RNN) is employed to solve real-time optimization problems with lower online computation. Regarding robustness, a new control law is developed, which comprises a fixed control gain (K) and a free perturbation (C). The proposed method enjoys a shrunken conservatism owing to the finding of a larger possible terminal region and using free control moves. The strategy is examined in an alkylation of benzene process and displays outstanding performance in both setpoint tracking and disturbance rejection problems. Moreover, the superiority of RNN over three conventional optimization algorithms is underlined in terms of MSE, the average time for solving the optimization problem, and the value of the cost function.