A multi-objective criterion and stability analysis for neural adaptive control of nonlinear MIMO systems: an experimental validation

被引：3

作者：

Atig, Asma ^{[1
,2
]}

Druaux, Fabrice ^{[3
]}

Lefebvre, Dimitri ^{[3
]}

Ben Abdennour, Ridha ^{[2
]}

机构：

[1] Tabuk Univ, King Faisal Rd, Tabuk 47512, Saudi Arabia

[2] Univ Gabes, Natl Sch Engineers Gabes, Lab Numer Control Ind Proc, Rue Omar Ibn Khattab, Gabes 6029, Tunisia

[3] Le Havre Univ, Elect & Automat Engn Res Grp GREAH, 75 Rue Bellot, F-76058 Le Havre, France

来源：

INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL | 2022年 / 16卷 / 3-4期

关键词：

multi-objective approach; adaptive control; stability analysis; recurrent neural networks; nonlinear systems; MIMO processes; SYNCHRONIZATION; DESIGN;

D O I：

10.1504/IJAAC.2022.122602

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a multi-objective indirect neural adaptive control design for nonlinear square multi-variable systems with unknown dynamics. The control scheme is made of an adaptive instantaneous neural emulator, a neural controller based on fully connected real-time recurrent learning (RTRL) networks and an online parameter updating law. A multi-objective criterion that takes into account the minimisation of the control energy is considered. The contribution of this paper is to develop a new controller parameter optimisation based on the Lyapunov stability analysis while ensuring control issues with environmental and economical objectives. Performance of the proposed approach in terms of regulation, tracking and minimisation of the control energy is evaluated by numerical simulations of a disturbed nonlinear multi-variable system. The obtained control scheme is then applied in real time to a disturbed MIMO thermal process.

引用

页码：433 / 458

页数：26

共 50 条

[1] Multi-Objective,Design of networking control systems for Nonlinear plants and stability analysis
Chong, G
Yun, L
[J]. PROCEEDINGS OF THE 5TH ASIA-PACIFIC CONFERENCE ON CONTROL & MEASUREMENT, 2002, : 185 - 190
[2] A multi-objective control approach for nonlinear systems
Gao, Chen
Liu, Hugh H. T.
Zhang, Ping
[J]. ICIA: 2009 INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION, VOLS 1-3, 2009, : 1326 - +
[3] Adaptive neural control of a class of MIMO nonlinear systems
Ge, SS
Wang, C
[J]. PROCEEDINGS OF THE 40TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-5, 2001, : 3530 - 3535
[4] Adaptive neural control of uncertain MIMO nonlinear systems
Ge, SS
Wang, C
[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2004, 15 (03): : 674 - 692
[5] Multi-objective command filtered adaptive control for nonlinear hydraulic active suspension systems
Ruolan Hao
Hongbin Wang
Shuang Liu
Mengke Yang
Zhijian Tian
[J]. Nonlinear Dynamics, 2021, 105 : 1559 - 1579
[6] Multi-objective command filtered adaptive control for nonlinear hydraulic active suspension systems
Hao, Ruolan
Wang, Hongbin
Liu, Shuang
Yang, Mengke
Tian, Zhijian
[J]. NONLINEAR DYNAMICS, 2021, 105 (02) : 1559 - 1579
[7] Multi-objective Nonlinear Control of Semiactive and Regenerative Systems
Scruggs, J. T.
[J]. 2010 AMERICAN CONTROL CONFERENCE, 2010, : 726 - 731
[8] Adaptive control and stability analysis of nonlinear systems using neural networks
Yamanaka, O
Yoshizawa, N
Ohmori, H
Sano, A
[J]. 1997 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, 1997, : 2424 - 2429
[9] Global adaptive neural tracking control of nonlinear MIMO systems
Jian Wu
Benyue Su
Jing Li
Xu Zhang
Liefu Ai
[J]. Neural Computing and Applications, 2017, 28 : 3801 - 3813
[10] Robust Adaptive Neural Control of a Class of MIMO Nonlinear Systems
胡亭亮
朱纪洪
孙增圻
[J]. Tsinghua Science and Technology, 2007, (01) : 14 - 21

← 1 2 3 4 5 →