Robust Control of Robot Manipulators using Particle Swarm Optimization Method

Abstract

In this paper, a new method for robust control is used. The whole robotic system, including the robot arm and motors in control, is considered. The main purpose of this article is to obtain the best results of the control law in order to achieve the minimum tracking error, which uses congestion optimization. Also, the designers of the control law are based on the nominal model . The real model uses intelligent systems. Control to resistance is evaluated by analysis and analysis.The stability of the system is demonstrated using Lyapunov's direct method, and the simulation results show the effectiveness of the proposed methods applied to a spherical robot driven by permanent magnet dc motors. Using the simulation results, the optimal values ​​of the parameters in the torque controllers have not converged to their true values ​​due to the large modelless dynamics, while they have converged to their true values ​​in the voltage control because it has only parametric uncertainty. . Also, the torque control law requires position vector, velocity vector and acceleration vector feedback.These feedback can not be easily obtained. In contrast, the law of voltage control requires feedback from the position vector, velocity vector, current vector, and time derivative. These feedback can be easily accessed.

Keywords

• particle swarm optimization
• robust control
• robot manipulators
• uncertainty