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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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HEPSO-SMC: a sliding mode controller optimized by hybrid enhanced particle swarm algorithm for manipulators.

Zhongwei Liu¹, Tianyu Zhang¹, Sibo Huang¹

¹School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan, 114051, China.

Scientific Reports

|May 13, 2025

Summary

This study introduces a Hybrid Enhanced Particle Swarm Optimization (HEPSO) to optimize Sliding Mode Controller (SMC) parameters. The HEPSO-SMC demonstrates superior effectiveness and robustness in control system applications, particularly for robotic manipulators.

Keywords:

CEC2022 Manipulator Parameter optimization Particle swarm optimization Sliding mode controller

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Area of Science:

Control Systems Engineering
Robotics
Computational Intelligence

Background:

Sliding Mode Controller (SMC) is a robust control design method.
Optimizing SMC parameters is crucial for enhancing system performance.
Existing optimization algorithms may have limitations in convergence speed and accuracy.

Purpose of the Study:

To develop a novel optimization algorithm, Hybrid Enhanced Particle Swarm Optimization (HEPSO), for SMC parameter tuning.
To evaluate the performance of the proposed HEPSO algorithm against other Particle Swarm Optimization (PSO) variants.
To validate the effectiveness and robustness of the optimized SMC (HEPSO-SMC) in a practical control system application, such as a 2-jointed manipulator.

Main Methods:

The HEPSO algorithm integrates adaptive inertia weightings (AIW), unified factor enhancement (UFE), and global optimal particle training (GOPT).
HEPSO performance was validated using the CEC2022 benchmark functions.
The HEPSO-SMC was applied to a 2-jointed manipulator for simulation verification and compared against PSO-SMC, IPSO-SMC, and UPS-SMC.

Main Results:

HEPSO demonstrated superior convergence speed and accuracy compared to other PSO variants on benchmark functions.
The HEPSO-SMC showed significant effectiveness and robustness in controlling a 2-jointed manipulator.
Comparative simulations confirmed the advantages of HEPSO-SMC over existing PSO-based SMC methods.

Conclusions:

The proposed HEPSO algorithm offers an effective approach for optimizing SMC parameters.
HEPSO-SMC provides enhanced performance, robustness, and stability for control systems.
This optimization strategy holds promise for advanced applications in robotics and automated control.