Approximate optimal control for an uncertain robot based on adaptive dynamic programming

被引：24
|
作者：
Kong, Linghuan [1 ,2 ,3 ]
Zhang, Shuang [1 ,2 ,3 ]
Yu, Xinbo [1 ,2 ,3 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
[2] Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China
[3] Univ Sci & Technol Beijing, Key Lab Knowledge Automat Ind Proc, Minist Educ, Beijing 100083, Peoples R China
来源：
NEUROCOMPUTING | 2021年 / 423卷
基金：
中国国家自然科学基金;
关键词：
Neural networks; Robot; Robust optimal control; ADP; NONLINEAR-SYSTEMS; CONTROL DESIGN; PARAMETER-ESTIMATION; TRACKING CONTROL; DEAD-ZONE; MANIPULATORS; TASKS;
D O I：
10.1016/j.neucom.2020.10.012
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An approximate optimal scheme is proposed for an uncertain n-link robot subject to saturation non linearity. A remarkable feature is that compared with the previous results, the model uncertainty in robotic dynamic is taken into account in the paper. Under the frame of adaptive dynamic programming (ADP), an optimal control is designed for the nominal robotic system and proved to be an approximate optimal control of the unknown robotic system, and it not only stabilizes the unknown system, but also decreases the control cost. Furthermore, the saturation non-linearity is also solved with generalized non quadratic functional. According to the Lyapunov theory, all the error signals can be proved to be uniformly ultimately bounded (UUB). Simulation examples are implemented to validate the effectiveness of the designed method. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页码：308 / 317
页数：10
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