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.

No abstract

No abstract

No abstract

No abstract