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Neural network adaptive terminal sliding mode trajectory tracking control for mechanical leg systems with uncertainty

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Abstract

This paper proposes an adaptive terminal sliding mode control based on neural block approximation for mechanical leg systems characterized by uncertainty and external disturbances. This control is based on a dynamic model of the mechanical leg and introduces an ideal system trajectory as a constraint. The structure of the paper is as follows. First, the RBF neural network is used to approximate the parameters of the dynamic model in blocks. This process is supplemented with a nonsingular terminal sliding mode surface to accelerate the convergence of tracking errors, and an adaptive law is used to adjust weights online to reconstruct the mechanical leg model. Next, an integral sliding mode control robust component is provided to mitigate external disturbances and correct model inaccuracies. Within this step, the Lyapunov method is used to prove the finite-time stability and uniform boundedness of the control system. Finally, the algorithm is validated and tested using the CAPACE rapid control system on a three-degree-of-freedom mechanical leg platform. The experimental results show that the proposed RBFTSM algorithm performs well in the performance evaluation of the MASE and RMSE values, with high trajectory tracking accuracy, anti-interference ability and strong robustness. Further evidence is presented to demonstrate the effectiveness and practicality of the proposed method.

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Data Availability

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the editor and anonymous reviewers for their instructive comments and feedback, and we appreciate your warm work earnestly.

Funding

This work is supported by the Guangxi Key Research and Development Program project Group State Epidemic Prevention Robot and Cluster Collaborative Control Platform (Grant No. Guike AB21220039).

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Authors and Affiliations

  1. School of Electrical Engineering, Guangxi University, Nanning, 530004, China

    Minbo Chen, Likun Hu & Zifeng Liao

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  1. Minbo Chen
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Contributions

Likun Hu: writing (reviewing, editing), supervision, project administration, funding acquisition, conceptualization. Minbo Chen: writing (original draft), validation, software, resources, methodology, investigation, formal analysis. Zifeng Liao: writing (reviewing, editing), visualization, software, data curation.

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Correspondence to Likun Hu.

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Chen, M., Hu, L. & Liao, Z. Neural network adaptive terminal sliding mode trajectory tracking control for mechanical leg systems with uncertainty. Appl Intell 55, 356 (2025). https://doi.org/10.1007/s10489-025-06228-4

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