Neuromuscular electrical stimulation (NMES) has been effectively applied in many rehabilitation treatments of individuals with spinal cord injury (SCI). In this context, we introduce a novel, robust, and intelligent control-based methodology to closed-loop NMES systems. Our approach utilizes a robust control law to guarantee system stability and machine learning tools to optimize both the controller parameters and system identification. Regarding the latter, we introduce the use of past rehabilitation data to build more realistic data-driven identified models. Furthermore, we apply the proposed methodology for the rehabilitation of lower limbs using a control technique named the robust integral of the sign of the error (RISE), an offline improved genetic algorithm optimizer, and neural network models. Although in the literature, the RISE controller presented good results on healthy subjects, without any fine-tuning method, a trial and error approach would quickly lead to muscle fatigue for individuals with SCI. In this paper, for the first time, the RISE controller is evaluated with two paraplegic subjects in one stimulation session and with seven healthy individuals in at least two and at most five sessions. The results showed that the proposed approach provided a better control performance than empirical tuning, which can avoid premature fatigue on NMES-based clinical procedures.

神经肌肉电刺激（NMES）已有效地应用于许多脊髓损伤（SCI）患者的康复治疗中。在这种情况下，我们为闭环NMES系统引入了一种新颖，强大，智能的基于控制的方法。我们的方法利用了强大的控制律来保证系统的稳定性，并利用机器学习工具来优化控制器参数和系统识别。关于后者，我们介绍了使用过去的康复数据来建立更现实的数据驱动的已识别模型。此外，我们将所提出的方法应用名为“错误迹象的鲁棒积分”（RISE）的控制技术，离线改进的遗传算法优化器和神经网络模型对下肢进行康复。虽然在文学中 RISE控制器在健康受试者上取得了良好的效果，无需任何微调方法，反复试验的方法会很快导致SCI患者的肌肉疲劳。在本文中，首次在一个刺激阶段对RISE控制器进行了评估，其中包括两个截瘫患者，并且在至少两个阶段且最多五个阶段中对七个健康个体进行了评估。结果表明，所提出的方法提供了比经验调整更好的控制性能，可以避免基于NMES的临床程序过早疲劳。在一个刺激疗程中对两名截瘫患者进行了评估，在至少两个疗程中至多五个疗程中对七个健康个体进行了评估。结果表明，所提出的方法提供了比经验调整更好的控制性能，可以避免基于NMES的临床程序过早疲劳。在一个刺激疗程中对两名截瘫患者进行评估，在至少两个疗程中至多五个疗程中对七个健康个体进行了评估。结果表明，所提出的方法提供了比经验调整更好的控制性能，可以避免基于NMES的临床程序过早疲劳。