Abstract
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.
| Original language | English |
|---|---|
| Article number | 8825558 |
| Pages (from-to) | 4443-4452 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans |
| Volume | 51 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 2021 |
| Externally published | Yes |
Funding
Manuscript received February 9, 2019; revised May 9, 2019; accepted August 16, 2019. Date of publication September 5, 2019; date of current version June 16, 2021. This work was supported by the National Natural Science Foundation of China under Grant 61320106010, Grant 61573190, and Grant 61571014. This manuscript has been co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doepublic-access-plan). This article was recommended by Associate Editor Z. Wang. (Corresponding author: Liping Yin.) L. Yin is with CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China (e-mail: [email protected]).
Keywords
- Data-driven
- Pareto distribution estimation (Pareto DE)
- entropy
- multiobjective optimization
- optimal operational control
- probability density function (PDF)