| Abstrak/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. |
