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An intelligent fixed-time super-twisting sliding mode control of the uncertain hybrid mechanism

Published online by Cambridge University Press:  03 October 2024

Xue Li Open the ORCID record for Xue Li [Opens in a new window]  and
Guoqin Gao Open the ORCID record for Guoqin Gao [Opens in a new window]
Xue Li
Affiliation:
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China
Guoqin Gao*
Affiliation:
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China
*
Corresponding author: Guoqin Gao; Email: [email protected]
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Abstract

In order to improve the global convergence performance of the super-twisting sliding mode control (STSMC) for the uncertain hybrid mechanism, especially in the high-speed scenario, and enhance the robustness of hybrid mechanism system to the uncertainties with a wide range of changes, an intelligent fixed-time super-twisting sliding mode control (IFTSTSMC) is proposed. Firstly, a fixed-time super-twisting sliding mode control (FTSTSMC) algorithm is designed by adding the exponential power terms with the fixed-time performance parameters in sliding variables and the transcendental function of the super-twisting algorithm in order to enhance the global convergence performance of the STSMC. Secondly, the existence condition of FTSTSMC for the uncertain hybrid mechanism is analyzed. The IFTSTSMC is designed by introducing RBF neural network to break through the limited range of uncertainties in FTSTSMC and enhance the robustness of hybrid mechanism system to the uncertainties with a wide range of changes. Then, the Lyapunov stability of the proposed method and the global fixed-time convergence of the system are proved theoretically. Finally, the effectiveness and superiority of the proposed control method are verified by the simulation and the automobile electro-coating conveying prototype experiment comparing with two classical finite-time sliding mode control methods.

Keywords

fixed-time convergencehybrid mechanismneural networksuper-twisting algorithmsliding mode control
Type
Research Article
Information
Robotica , Volume 42 , Issue 10 , October 2024 , pp. 3532 - 3551
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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