Passive control strategy of lower limb rehabilitation robot based on RBF neural network

Yihang Ma; Jirong Wang

doi:10.1117/12.2680818

23 May 2023 Passive control strategy of lower limb rehabilitation robot based on RBF neural network

Yihang Ma, Jirong Wang

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126454J (2023) https://doi.org/10.1117/12.2680818
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

In practical application, the model of a lower limb rehabilitation robot has uncertain parameters and external interference. The RBF neural network is employed to fit the uncertain part of the system and the sliding mode approach rate is designed to compensate for the external interference and the deviation of the neural network in this paper. The conventional sliding mode control uses a discontinuous symbolic function, which causes chatting phenomenon in the output of the controller. In this paper, a new sliding mode approximation rate is proposed to reduce the jitter phenomenon in the system output, and the Lyapunov method is used to ensure the final asymptotic stability. Finally, the effectiveness of the algorithm is verified by the Simulink.

Citation Download Citation

Yihang Ma and Jirong Wang "Passive control strategy of lower limb rehabilitation robot based on RBF neural network", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126454J (23 May 2023); https://doi.org/10.1117/12.2680818

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