Adaptive Neural Control of Uncertain Nonlinear Systems Using Disturbance Observer

被引：228
|
作者：
Chen, Mou [1 ,2 ]
Shao, Shu-Yi [1 ,2 ]
Jiang, Bin [1 ,2 ]
机构：
[1] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 210016, Jiangsu, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, Jiangsu Key Lab Internet Things & Control Technol, Nanjing 210016, Jiangsu, Peoples R China
来源：
IEEE TRANSACTIONS ON CYBERNETICS | 2017年 / 47卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Disturbance observer; input saturation; MIMO nonlinear system; neural network (NN); tracking control; DYNAMIC SURFACE CONTROL; OUTPUT-FEEDBACK CONTROL; TRACKING CONTROL; NETWORK CONTROL; MOTION CONTROL; DELAY SYSTEMS; DESIGN;
D O I：
10.1109/TCYB.2017.2667680
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 studies the problem of prescribed performance adaptive neural control for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems in the presence of external disturbances and input saturation based on a disturbance observer. The system uncertainties are tackled by neural network (NN) approximation. To handle unknown disturbances, a Nussbaum disturbance observer is presented. By incorporating the disturbance observer and NNs, an adaptive prescribed performance neural control scheme is further developed. Then, the expected asymptotically convergent tracking errors between system output signals and desired signals are achieved. Numerical simulation results demonstrate the effectiveness of the proposed control scheme.
引用
收藏
页码：3110 / 3123
页数：14
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