Exoskeleton robot–based neurorehabilitation has received a lot of attention recently due to positive evidence supporting its ability to provide different forms of physical therapy and in helping evaluate the patient recovery rate accurately. The performance of exoskeleton robot–based physical therapy depends on the accuracy of the motion control system. While the computed torque control scheme based on inverse dynamics is ideal from a theoretical perspective, the stability and tracking performance strongly depends on the model accuracy. Expecting a deterministic payload for a rehabilitation robot is impractical, which makes the computed torque controller unrealistic for such an application. In this article, a 7-degree-of-freedom human lower extremity dynamic model is developed using the Lagrange method and a novel Model Reference Computed Torque Controller is utilized for control. The computed torque controller is used to estimate the joint torque requirements for tracking a trajectory. Calculated joint torques are applied to a similarly structured plant with different parameters. The deviation of the plant from the model is calculated. A proportional–integral–derivative controller is employed to force the plant to behave like the robot model. A realistic friction model is incorporated to simulate joint friction in the plant. The stability and tracking performance of the control system is presented for sequential as well as simultaneous joint movements. To verify the robustness of the developed controller, analysis of variance statistical technique is used.

基于外骨骼机器人的神经康复治疗最近受到了广泛的关注，这是因为有积极的证据支持其提供不同形式的物理疗法并有助于准确评估患者康复率的能力。基于外骨骼机器人的物理疗法的性能取决于运动控制系统的准确性。虽然从理论上讲，基于逆动力学的转矩控制方案是理想的，但稳定性和跟踪性能在很大程度上取决于模型的准确性。为康复机器人期望确定的有效载荷是不切实际的，这使得计算出的转矩控制器对于这种应用而言是不现实的。在本文中，使用拉格朗日方法开发了一个7自由度的人体下肢动态模型，并使用新型的模型参考计算转矩控制器进行控制。计算出的扭矩控制器用于估计用于跟踪轨迹的关节扭矩需求。计算得出的关节扭矩将应用于具有不同参数的结构相似的工厂。计算植物与模型的偏差。使用比例-积分-微分控制器来强制设备像机器人模型一样运行。结合了实际的摩擦模型来模拟工厂中的关节摩擦。提出了控制系统的稳定性和跟踪性能，可用于顺序运动以及同时进行的联合运动。为了验证开发的控制器的鲁棒性，