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  1. A hybrid exoskeleton comprising a powered exoskeleton and functional electrical stimulation (FES) is a promising technology for restoration of standing and walking functions after a neurological injury. Its shared control remains challenging due to the need to optimally distribute joint torques among FES and the powered exoskeleton while compensating for the FES-induced muscle fatigue and ensuring performance despite highly nonlinear and uncertain skeletal muscle behavior. This study develops a bi-level hierarchical control design for shared control of a powered exoskeleton and FES to overcome these challenges. A higher-level neural network–based iterative learning controller (NNILC) is derived to generate torques needed to drive the hybrid system. Then, a low-level model predictive control (MPC)-based allocation strategy optimally distributes the torque contributions between FES and the exoskeleton’s knee motors based on the muscle fatigue and recovery characteristics of a participant’s quadriceps muscles. A Lyapunov-like stability analysis proves global asymptotic tracking of state-dependent desired joint trajectories. The experimental results on four non-disabled participants validate the effectiveness of the proposed NNILC-MPC framework. The root mean square error (RMSE) of the knee joint and the hip joint was reduced by 71.96 and 74.57%, respectively, in the fourth iteration compared to the RMSE in the 1st sit-to-stand iteration. 
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  3. Abstract Introduction

    Optimal frequency modulation during functional electrical stimulation (FES) may minimize or delay the onset of FES‐induced muscle fatigue.


    An offline dynamic optimization method, constrained to a modified Hill‐Huxley model, was used to determine the minimum number of pulses that would maintain a constant desired isometric contraction force.


    Six able‐bodied participants were recruited for the experiments, and their quadriceps muscles were stimulated while they sat on a leg extension machine. The force–time (F–T) integrals and peak forces after the pulse train was delivered were found to be statistically significantly greater than the force–time integrals and peak forces obtained after a constant frequency train was delivered.


    Experimental results indicated that the optimized pulse trains induced lower levels of muscle fatigue compared with constant frequency pulse trains. This could have a potential advantage over current FES methods that often choose a constant frequency stimulation train.Muscle Nerve57: 634–641, 2018

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