Simulation and Analysis of an Adaptive Manipulator Based on Machine Learning
DOI:
https://doi.org/10.54097/4et41m82Keywords:
Machine Learning; PID; 3-DOF Manipulator; Genetic Algorithm.Abstract
This study takes the PUMA560 manipulator as the research object, selects its first three rotating joints to construct a 3-degree-of-freedom (3-DOF) manipulator system, and aims to improve the control accuracy of the manipulator and environmental adaptability. First, the Denavit-Hartenberg (DH) parameter method is used to establish the manipulator’s kinematic model; the MATLAB Robotics Toolbox is employed to define the link coordinate system, joint angles, link lengths, and other DH parameters, and a velocity ellipsoid is constructed to analyze the motion characteristics of the end effector. In the trajectory planning stage, the Joint Trajectory Planning (JTRAJ) function (for smooth interpolation in joint space) and the Cartesian Trajectory Planning (CTRAJ) function (for linear path interpolation at the end effector) are compared, and finally, the JTRAJ function is selected to plan the target path. To achieve precise control of the manipulator, a PID (Proportional-Integral-Derivative) control simulation model based on Simulink is built. Simulation results show that compared with manual parameter tuning, the PID control optimized by Genetic Algorithm (GA) has a smaller error fluctuation range, and the error tends to be stable at the end of the simulation, significantly improving the manipulator’s control robustness and trajectory accuracy. This study realizes the effective integration of machine learning and traditional control algorithms, providing a feasible solution for the optimization of manipulator adaptive control; future research can further explore the influence of parameter ranges and fitness function definitions on control effects, and improve data horizontal comparison to deepen the research.
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