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

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 (...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 423; pp. 308 - 317
Main Authors Kong, Linghuan, Zhang, Shuang, Yu, Xinbo
Format Journal Article
LanguageEnglish
Published Elsevier B.V 29.01.2021
Subjects
ADP
Neural networks
Robot
Robust optimal control
Neural networks
Robot
ADP
Robust optimal control
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2020.10.012