Bao Xuefeng, Kirsch Nicholas, Dodson Albert, Sharma Nitin
Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 15261.
IAM Robotics, Sewickley, PA 15143.
J Comput Nonlinear Dyn. 2019 Oct 1;14(10):101009-1010097. doi: 10.1115/1.4042903. Epub 2019 Sep 9.
Functional electrical stimulation (FES) is prescribed as a treatment to restore motor function in individuals with neurological impairments. However, the rapid onset of FES-induced muscle fatigue significantly limits its duration of use and limb movement quality. In this paper, an electric motor-assist is proposed to alleviate the fatigue effects by sharing work load with FES. A model predictive control (MPC) method is used to allocate control inputs to FES and the electric motor. To reduce the computational load, the dynamics is feedback linearized so that the nominal model inside the MPC method becomes linear. The state variables: the angular position and the muscle fatigue are still preserved in the transformed state space to keep the optimization meaningful. Because after feedback linearization the original linear input constraints may become nonlinear and state-dependent, a barrier cost function is used to overcome this issue. The simulation results show a satisfactory control performance and a reduction in the computation due to the linearization.
功能性电刺激（FES）被规定为一种治疗方法，用于恢复神经功能受损个体的运动功能。然而，FES诱导的肌肉疲劳迅速出现，显著限制了其使用持续时间和肢体运动质量。在本文中，提出了一种电动助力装置，通过与FES分担工作负荷来减轻疲劳影响。采用模型预测控制（MPC）方法将控制输入分配给FES和电动马达。为了减少计算负荷，对动力学进行反馈线性化，使得MPC方法中的标称模型变为线性。状态变量：角位置和肌肉疲劳仍保留在变换后的状态空间中，以使优化有意义。因为反馈线性化后，原来的线性输入约束可能会变成非线性且依赖于状态，所以使用障碍成本函数来克服这个问题。仿真结果显示了令人满意的控制性能，并且由于线性化而减少了计算量。
