Enhanced Equation Discovery of 3-DoF Robotic Manipulator Dynamics Using LASSO Model Selection Criteria With Variable Segregation Algorithm

Abstract

The challenge in controlling a manipulator robot is to model the system to obtain an efficient control system design. One approach that can be used to model the dynamics of a manipulator robot is data-driven modeling. However, in its implementation, data-driven modeling is highly sensitive to sensor noise, which significantly affects the accuracy of the system identification. In addition, the existing approach yields only a generalized form of the differential equation for each joint, which has not been divided into inertial, Coriolis, and gravitational variables that can be used for other purposes. In this study, a LASSO model selection criteria with a variable segregation algorithm (LMSCVS) is proposed to derive the dynamic equation of a 3-DoF manipulator robot, segregating the generalized form variables into Coriolis and centrifugal, inertia, and gravitational variables. Additionally, a Dynamic Expression Nonlinearization (DEx-N) algorithm is introduced to generate nonlinear candidates more efficiently to express the dynamics of the robot manipulator. The experimental results on the ROB3 hardware demonstrate that the proposed method successfully discovers mathematical equations, resulting in higher accuracy and sparsity compared to the previous method. The processing time of the proposed method is also significantly faster. Based on these results, the proposed method has a better performance in identifying real systems that usually have noise in the sensor data and in discovering the equation of robot manipulator dynamics for broader purposes.

Keywords

• Dynamic system
• equation discovery
• robot manipulators
• sensor noise
• system identification