期刊
ISA TRANSACTIONS
卷 53, 期 4, 页码 983-993出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2014.05.003
关键词
Non-minimum phase; Multi-input-multi-output; Pseudo-plant; Neural network inverse control; RBFN disturbance observer
资金
- Science Foundation of Jiangsu Province [BK20130018, 13KJB460015, BK2012327]
- National Natural Science Foundation of China [61203011, 91016004]
- Ministry of Education [APCLI1007]
- PhD Programs Foundation of Ministry of Education of China [20120092120031]
- Foundation of Yangzhou University [2013CXJ034]
An adaptive control strategy combining neural network inverse controller (NNIC) with RBFN disturbance observer (RBFNDOB) is developed for a multi-input-multi-output (MIMO) system with non-minimum phase, internal and external disturbances in this paper. Since the inverse model of system is unstable due to the non-minimum phase, a pseudo-plant is constructed, then the RBFN is used to identify the inverse model of pseudo-plant, which can track the parameter variations of system. By copying the structure and parameters of the identifier, the NNIC is obtained. Cascading the NNIC with the original plant, the MIMO system can be decoupled and linearized into independent SISO systems. For the independent decoupled system, the RBFNDOB employs a RBFN to observe the external disturbances and this estimate value is used as a feed-forward compensation term in controller. The case study on ball mill grinding circuit is presented. The effectiveness of the proposed method is demonstrated by simulation results and comparisons. (c) 2014 ISA. Published by Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据