Developing a Linear Quadratic Regulator for Human Lower Extremity Exoskeleton Robot

During the last two decades, exoskeleton robot-assisted neurorehabilitation has received a lot of attention. The major reason for active research in robot-assisted rehabilitation is its ability to provide various types of physical therapy at different stages of physical and neurological recovery. The performance of the robot-assisted physical therapy is greatly influenced by the robot motion control system. Robot dynamics are nonlinear, but many linear control schemes can adequately handle the nonlinear dynamics with the help of feedback linearization techniques. In this study, the dynamic model of the human lower extremities was developed. A state-space form of the human lower extremity nonlinear dynamic model is presented. LuGre friction model was used to simulate the robot joint friction. A Linear Quadratic Regulator (LQR) was designed to control the human lower extremity dynamics. Dynamic simulations were carried out in the Matlab-Simulink environment. The designed controller's tracking performance was demonstrated in the presence of joint friction. The developed controller’s tracking performance is assessed by comparing the results obtained using LQR with other linear and nonlinear controllers (PID, Computed torque control, and Sliding mode control). For performance verification, the same robot dynamics, friction model, and trajectories were used. The stability of the developed control system is also analyzed.