文献类型：期刊文献

英文题名：Neural Dynamics for Control of Industrial Agitator Tank With Rapid Convergence and Perturbations Rejection

作者：Duan, Wenhui[1,4];Xiao, Xiuchun[2];Fu, Dongyang[2];Yan, Jingwen[3];Liu, Mei[1];Zhang, Jiliang[1];Jin, Long[1,4]

机构：[1]Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China;[2]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[3]Shantou Univ, Coll Engn, Shantou 515063, Peoples R China;[4]Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China

年份：2019

卷号：7

起止页码：102941

外文期刊名：IEEE ACCESS

收录：SCI-EXPANDED(收录号：WOS:000481688500206)、、EI(收录号：20205009615964)、Scopus(收录号：2-s2.0-85082241001)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61703189, in part by the International Science and Technology Cooperation Program of China under Grant 2017YFE0118900, in part by the Key Laboratory of Digital Signal and Image Processing of Guangdong Province under Grant 2016GDDSIPL-02, in part by the Doctoral Initiating Project of Guangdong Ocean University under Grant E13428, in part by the Innovation and Strength Project of Guangdong Ocean University under Grant Q15090 and Grant 230419065, in part by the Natural Science Foundation of Gansu Province, China, under Grant 18JR3RA264, in part by the Sichuan Science and Technology Program under Grant 19YYJC1656, in part by the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences under Grant 20190112, and in part by the Fundamental Research Funds for the Central Universities under Grant lzujbky-2019-89.

语种：英文

外文关键词：Chemical industry; automatic control; control design; neural dynamics method; rapid convergence; perturbations rejection

外文摘要：The industrial agitator tank is a widely used equipment in the chemical industry for the production of the chemical reagents. The high-performance agitator tank controller is critical to increase its productivity. In this paper, we propose an agitator tank controller based on a neural dynamics method with a shorter error-converging time in comparison with the existing methods. In addition, the controller also has a strong capability to reject perturbations. Furthermore, the superiority of the proposed agitator tank controller is theoretically analyzed. Ultimately, computer simulations synthesized by the proposed agitator tank controller are conducted. The numerical results validate the superior performance of the proposed controller.