Exoskeleton robot-assisted physical therapy has recently been studied extensively due to its proven effectiveness in providing different forms of physical therapy at any stage of physical recovery. The efficacy of robot-assisted physical therapy depends on the maneuverability of the robot during the rehabilitation application. Robot dynamics is inherently nonlinear. Often, robot control algorithms are developed based on an approximate robot dynamic model which leads to system instability and tracking errors. Accurately determining a rehabilitation robot's payload (human limb masses and inertial properties) is frequently impractical. An adaptive control scheme can handle modeling errors very efficiently. In this paper, a 7 degrees of freedom (DOF) human lower extremity dynamic model was developed using the Newton Euler’s method. To simulate joint friction, a realistic friction model is included. A direct adaptive controller is designed so that the robot can follow the prescribed trajectory with high speed and accuracy. A total of 31 model parameters were considered for adaption. To ensure system stability, the controller's adaptive gains are determined based on the Lyapunov stability approach. Simulation results show excellent tracking performance of the developed controller in the presence of joint friction.

外骨骼机器人辅助的物理治疗方法最近得到了广泛的研究，这是由于其在物理恢复的任何阶段都可提供不同形式的物理治疗方法的有效性。机器人辅助物理治疗的有效性取决于康复应用过程中机器人的可操作性。机器人动力学本质上是非线性的。通常，基于近似的机器人动态模型开发机器人控制算法，这会导致系统不稳定和跟踪错误。准确确定康复机器人的有效载荷（人体肢体质量和惯性）通常是不切实际的。自适应控制方案可以非常有效地处理建模错误。在本文中，使用牛顿·欧拉（Newton Euler）方法开发了一个7自由度（DOF）人体下肢动力学模型。为了模拟关节摩擦，包括了真实的摩擦模型。设计了直接自适应控制器，以便机器人可以高速且准确地遵循规定的轨迹。总共考虑了31个模型参数进行调整。为确保系统稳定性，控制器的自适应增益是基于Lyapunov稳定性方法确定的。仿真结果表明，在存在关节摩擦的情况下，开发的控制器具有出色的跟踪性能。