Data-driven adaptive predictive control of hydrocracking process using a covariance matrix adaption evolution strategy

Abstract

In this paper, a new data-driven adaptive predictive control architecture to the reactor temperature control problem in hydrocracking process is proposed with completely unknown system dynamics. Specifically, the nonlinear relationship between the reactor temperature and conversion is established using echo state networks (ESNs) with an intrinsic plasticity rule, and a covariance matrix adaption evolution strategy (CMA-ES) is used to optimize the reactor temperature iteratively using the input and state information. Besides, the prescribed performance function is introduced to guarantee the transient and steady performance of the system. In addition, the input constraints and external disturbance are simultaneously considered. Numerical simulation results and industrial application prove the efficacy of the proposed method.