文献类型：期刊文献

英文题名：Learning-Based Adaptive Optimal Control for Flotation Processes Subject to Input Constraints

作者：Li, Zhongmei[1];Huang, Mengzhe[2];Zhu, Jianyong[3];Gui, Weihua[4];Jiang, Zhong-Ping[2];Du, Wenli[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]NYU, Tandon Sch Engn, Brooklyn, NY 11201 USA;[3]East China Jiaotong Univ, Sch Elect & Automat Engn, Nanchang 330013, Jiangxi, Peoples R China;[4]Cent South Univ, Sch Automat, Changsha 410083, Peoples R China

年份：2023

卷号：31

期号：1

起止页码：252

外文期刊名：IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY

收录：;EI(收录号：20222012127936);WOS:【SCI-EXPANDED(收录号:WOS:000795110400001)】；

基金：This work was supported in part by the National Natural Science Foundation of China through the Basic Science Center Program under Grant 61988101, in part by the National Science Fund for Distinguished Young Scholars under Grant 61925305, in part by the Shanghai Sailing Program under Grant 20YF1411000, and in part by the National Natural Science Foundation of China under Grant 62003140. Recommended by Associate Editor F. You.

语种：英文

外文关键词：Process control; Antimony; Feature extraction; Indexes; Chemicals; Adaptation models; Predictive models; Actuator saturation; adaptive dynamic programming (ADP); deep learning (DL) model; flotation processes; reagents' control

摘要：This article presents a learning-based adaptive optimal control approach for flotation processes subject to input constraints and disturbances using adaptive dynamic programming (ADP) along with double-loop iteration. First, the principle of the operational pattern is adopted to preset reagents' addition based on the feeding condition. Then, this article leverages a deep learning model, which is composed of multiple neural layers to detect flotation indexes directly from the raw froth images. After that, the tracking error between the detected flotation indexes and the reference values can be minimized by using ADP-based double-loop iteration. Particularly, a policy-iteration (PI) method is utilized for the proposed learning-based ADP algorithm. In the inner loop, the optimal control problem is formulated as a linear quadratic regulator (LQR) problem using the low-gain feedback design method. In the outer loop, the design parameters, i.e., weighting matrices, are tuned automatically to satisfy the input constraints. Finally, the analytical results demonstrate that the proposed scheme can guarantee asymptotic tracking in the presence of actuator saturation and disturbances.