Data-Driven Pareto-DE-Based Intelligent Optimal Operational Control for Stochastic Processes

In this article, the optimal operational control problem is considered for complex industrial processes with stochastic disturbances. The performance index is optimized by set points reselection on the operational control layer together with controllers design on the loop control layer. First, the operational indices are obtained through some optimization algorithms. Second, the controllers are designed in the ideal situation to ensure that the controlled variables can track desired set points. To minimize the performance deterioration caused by non-Gaussian stochastic noises or disturbances, a novel Pareto distribution estimation (Pareto DE)-based intelligent set-points reselection approach is proposed to optimize entropy and expectation simultaneously. In the proposed method, entropy is formulated in a recursive way basing on joint PDFs which are obtained through multivariate kernel density and bandwidth selection. Meanwhile, both the controller structure and controller parameters are fixed for whatever disturbances acting on the system. Finally, simulations are given to illustrate the effectiveness of the proposed strategy.