Lower limb exoskeletons play a pivotal role in augmenting human mobility and improving the quality of life for individuals with mobility impairments. In light of these pressing needs, this paper presents an improved control strategy for a 10-degree-of-freedom lower limb exoskeleton, with a particular focus on enhancing stability, precision, and robustness. To simplify the intricate dynamic model of the exoskeleton, our approach leverages a more manageable 2nd order ultra-local model. We employ two radial basis function (RBF) neural networks to accurately estimate both lumped disturbances and non-physical parameters associated with this ultra-local model. In addition, our control strategy integrates the backstepping technique and the super twisting algorithm to minimize tracking errors. The stability of the designed controller is rigorously established using Lyapunov theory. In the implementation phase, a virtual prototype of the exoskeleton is meticulously designed using SolidWorks and then exported to Matlab/Simscape Multibody for co-simulation. Furthermore, the desired trajectories are derived from surface electromyography (sEMG) measured data, aligning our control strategy with the practical needs of the user. Comprehensive experimentation and analysis have yielded compelling numerical findings that underscore the superiority of our proposed method. Across all 10 degrees of freedom, our controller demonstrates a significant advantage over alternative controllers. On average, it exhibits an approximately 45% improvement compared to the Adaptive Backstepping-Based -RBF Controller, a 74% improvement compared to the Model-Free Based Back-Stepping Sliding Mode Controller, and an outstanding 74% improvement compared to the Adaptive Finite Time Control Based on Ultra-local Model and Radial Basis Function Neural Network. Furthermore, when compared to the PID controller, our approach showcases an exceptional improvement of over 80%. These significant findings underscore the effectiveness of our proposed control strategy in enhancing lower limb exoskeleton performance, paving the way for advancements in the field of wearable robotics.

下肢外骨骼在增强人类活动能力和改善行动障碍人士的生活质量方面发挥着关键作用。鉴于这些迫切的需求，本文提出了一种改进的10自由度下肢外骨骼控制策略，特别注重增强稳定性、精度和鲁棒性。为了简化外骨骼复杂的动态模型，我们的方法利用了更易于管理的二阶超局部模型。我们采用两个径向基函数（RBF）神经网络来准确估计与该超局部模型相关的集总扰动和非物理参数。此外，我们的控制策略集成了反步技术和超扭曲算法，以最大限度地减少跟踪误差。所设计的控制器的稳定性是使用李亚普诺夫理论严格建立的。在实施阶段，利用SolidWorks精心设计外骨骼的虚拟原型，然后导出到Matlab/Simscape Multibody进行联合仿真。此外，所需的轨迹来自表面肌电图 (sEMG) 测量数据，使我们的控制策略与用户的实际需求保持一致。综合实验和分析得出了令人信服的数值结果，强调了我们提出的方法的优越性。在所有 10 个自由度上，我们的控制器比其他控制器表现出显着的优势。平均而言，与基于自适应反步的 RBF 控制器相比，它的性能提高了约 45%；与基于无模型的反步滑模控制器相比，它的性能提高了 74%；与自适应有限控制器相比，它的性能提高了 74%。基于超局部模型和径向基函数神经网络的时间控制。此外，与 PID 控制器相比，我们的方法显示出超过 80% 的卓越改进。这些重大发现强调了我们提出的控制策略在增强下肢外骨骼性能方面的有效性，为可穿戴机器人领域的进步铺平了道路。