Adaptive Control for End-Effector Exoskeleton Robots With Unknown Dynamics and Actuator Parameters
提出一种自适应反步控制器，无需基函数即可处理外骨骼机器人的未知动力学和执行器参数，通过李雅普诺夫分析保证稳定性，并在工业机器人和可穿戴机器人上验证了有效性。
Exoskeleton robots hold immense promise in rehabilitation, serving as crucial aids for patient mobility and exercise. However, harnessing their capabilities requires overcoming significant control challenges arising from complex nonlinear dynamics and uncertainties in both models and actuators. This article introduces a novel adaptive backstepping controller designed specifically for exoskeleton robots navigating uncertain dynamics and actuator parameters. Unlike conventional approaches that rely on basis functions, the proposed controller (PL) integrates a modified function approximation technique (MFAT) to approximate dynamic parameters without the need for such functions. The MFAT effectively manages mismatched perturbations, while the backstepping control compensates for uncertainties associated with state variables, enhancing resilience to disturbances, especially in scenarios where the exoskeleton’s dynamics model is unknown. The Lyapunov stability analysis ensures uniformly ultimately bounded (UUB) signals within the closed-loop system. A comparative study conducted on the industrial robot IRB 120 validates the effectiveness of the PL and highlights its superior performance. Real-time implementation on a seven degrees of freedom (DOFs) wearable robot named ETS-MARSE confirms the efficiency of the control algorithm. The results from simulations and experiments underscore the efficacy of the proposed approach. The insights gained from this article pave the way to unlocking the full potential of exoskeleton robots in rehabilitation settings, promising improved patient outcomes and advancing human-technology interaction to new heights.