Parameters Identification Based on PD Controller for A 2-DOF Robotic Arm System

This paper presented a least-squares method for the parameter identification of a 2-degree-of-freedom (2-DOF) robotic arm controlled by a PD controller using brushless DC motors (BLDC) in practice. Due to the complexity of the 2-DOF manipulator using BLDC, establishing its dynamic equations becomes particularly challenging because of the influence of nonlinear factors such as friction, varying inertia, and joint interactions. Hence, the Least Squares (LS) technique is employed as a means to estimate the model's dynamic parameters. The process begins with modeling and designing the robot in SolidWorks, followed by simulations conducted in MATLAB using the Simscape toolbox. The kinematics are subsequently constructed using the Modified Denavit-Hartenberg (MDH) approach, and the dynamic model is derived using the Lagrange-Euler equations. Subsequently, the grouped dynamic parameters are arranged into a general linear matrix format to facilitate the application of the Least Squares method. Data for parameter estimation is collected using a PD controller through MATLAB Simulink simulations of both the SolidWorks-based robot model and the real-world system. To assess the accuracy of the estimated parameters, the Root Mean Squared Error (RMSE) metric is employed to analyze the simulation outcomes. The proposed approach is validated through simulations performed on a 2-DOF robotic arm system driven by brushless DC motors.