Optimization of sliding control parameters for a 3-dof robot arm using genetic algorithm (GA)

TL;DR

This paper introduces a genetic algorithm-based approach to optimize sliding mode control parameters for a 3-DOF robot arm, enhancing tracking accuracy and reducing chattering under uncertain conditions.

Contribution

It proposes a novel optimization method combining genetic algorithms with sliding mode control to improve robot arm performance.

Findings

Enhanced tracking accuracy demonstrated in simulations

Reduced chattering effect compared to conventional methods

Genetic algorithm outperforms traditional parameter tuning

Abstract

This paper presents a method for optimizing the sliding mode control (SMC) parameter for a robot manipulator applying a genetic algorithm (GA). The objective of the SMC is to achieve precise and consistent tracking of the trajectory of the robot manipulator under uncertain and disturbed conditions. However, the system effectiveness and robustness depend on the choice of the SMC parameters, which is a difficult and crucial task. To solve this problem, a genetic algorithm is used to locate the optimal values of these parameters that gratify the capability criteria. The proposed method is efficient compared with the conventional SMC and Fuzzy-SMC. The simulation results show that the genetic algorithm with SMC can achieve better tracking capability and reduce the chattering effect.