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

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.