🤖 AI Summary
To address low trajectory tracking accuracy and severe chattering in conventional sliding mode control (SMC) for a 3-DOF robotic manipulator under model uncertainties and external disturbances, this paper proposes a robust control strategy that employs a genetic algorithm (GA) to optimize key SMC parameters. Leveraging its global search capability, the GA automatically tunes critical parameters—including the switching gain and boundary layer thickness—thereby preserving strong robustness while significantly mitigating chattering. Unlike traditional and fuzzy SMC approaches, the proposed method requires no prior knowledge and enables adaptive parameter adjustment. Simulation results demonstrate substantial improvements: trajectory tracking error is reduced by approximately 42%, and control chattering is markedly suppressed. The approach thus achieves a favorable balance among high tracking precision, strong robustness against uncertainties and disturbances, and practical implementability in engineering applications.
📝 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.