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Volume 11 Issue 3
Mar.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(3): 760-781
D. He, H. P. Wang, Y. Tian, and  Y. Guo,  “A fractional-order ultra-local model-based adaptive neural network sliding mode control of n-DOF upper-limb exoskeleton with input deadzone,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 760–781, Mar. 2024. doi: 10.1109/JAS.2023.123882
Citation: D. He, H. P. Wang, Y. Tian, and  Y. Guo,  “A fractional-order ultra-local model-based adaptive neural network sliding mode control of n-DOF upper-limb exoskeleton with input deadzone,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 760–781, Mar. 2024. doi: 10.1109/JAS.2023.123882
shu

A Fractional-Order Ultra-Local Model-Based Adaptive Neural Network Sliding Mode Control of n-DOF Upper-Limb Exoskeleton With Input Deadzone

doi: 10.1109/JAS.2023.123882
Funds:  This work was supported in part by the National Natural Science Foundation of China (62173182, 61773212) and the Intergovernmental International Science and Technology Innovation Cooperation Key Project of Chinese National Key R&D Program (2021YFE0102700)
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    Abstract

  • This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties, external disturbances and input deadzone. Considering the model complexity and input deadzone, a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design. Firstly, the control gain of ultra-local model is considered as a constant. The fractional-order sliding mode technique is designed to stabilize the closed-loop system, while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance. Correspondingly, a fractional-order ultra-local model-based neural network sliding mode controller (FO-NNSMC) is proposed. Secondly, to avoid disadvantageous effect of improper gain selection on the control performance, the control gain of ultra-local model is considered as an unknown parameter. Then, the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain. Correspondingly, a fractional-order ultra-local model-based adaptive neural network sliding mode controller (FO-ANNSMC) is proposed. Moreover, the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory. Finally, with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton, the obtained compared results illustrate the effectiveness and superiority of the proposed method.

     

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    Highlights

    • A fractional-order ultra-local model is proposed to re-formulate the complex exoskeleton system
    • Time-delay estimation is combined with neural network to eliminate the lumped disturbance
    • Nussbaum technique is introduced into model-free controller to make control gain adaptive
    • Experiments on a 2-DOF upper-limb exoskeleton are realized to verify the effectiveness

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