Friction Parameter Identification and Feedforward Compensation for Joint Motors in Lower Limb Rehabilitation Robots

The inherent nonlinear characteristics of friction adversely affects the control accuracy of joint motor drive systems in lower limb rehabilitation robots. Recognizing this challenge, this study proposes an improved friction model and further designs a feedforward compensation control scheme to mitigate motor friction on the basis of the friction model. Compensating for motor control utilizing the friction model, typically the Stribeck friction model, is a promising solution. To overcome the inherent limitation discontinuities of the Stribeck friction model, this study introduces an improved friction model by incorporating the sigmoid function into it. The friction parameter of the model is identified based on the data collected during the experiment, specifically the motor velocity and current. And to enhance the precision of the parameter identification, Kalman filtering algorithm is applied to mitigate the Gaussian noise generated during the experiment. Subsequently, the firefly algorithm is employed for offline identification and curve fitting of the friction parameters in the improved model. Based on the improved friction model, a feedforward compensation controller is further designed by integrating the traditional three-closed-loop PID motor control method with real-time friction compensation. The system employs humanoid gait patterns as input signals to achieve precise position tracking of the robot’s joint motors. Compared with the conventional PID control, the proposed feedforward compensation control reduces both position and current tracking errors. These results confirm that the feedforward compensation strategy, based on the refined Stribeck friction model, effectively mitigates the adverse effects of nonlinear friction, thereby improving the control performance of joint motor drive systems.