HEPSO-SMC：一种基于混合增强粒子群算法优化的机器人滑模控制器。

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-05-13 DOI:10.1038/s41598-025-00728-6

Zhongwei Liu, Tianyu Zhang, Sibo Huang, He Wang

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引用次数: 0

摘要

滑模控制器（SMC）是一种用于控制系统的控制器设计方法，旨在实现系统的鲁棒稳定控制。为了提高SMC的性能，本文采用混合增强粒子群优化算法（HEPSO）对SMC （HEPSO-SMC）的[公式：见文]、[公式：见文]、[公式：见文]、[公式：见文]、[公式：见文]等参数进行优化。HEPSO集成了三种策略：自适应惯性加权（AIW）、统一因子增强（UFE）和全局最优粒子训练（GOPT）。通过包含12个基准函数的CEC2022仿真验证了该算法的有效性，结果表明，该算法在收敛速度和精度方面都优于其他PSO算法。采用HEPSO-SMC作为二关节机械手进行仿真验证。仿真结果与PSO-SMC、IPSO-SMC和UPS-SMC进行了比较，验证了HEPSO-SMC的有效性和鲁棒性。

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

Sliding Mode Controller (SMC) is a controller design method used for control systems, aimed at achieving robust and stable control of systems. To improve the performance of SMC, this paper applies a hybrid enhanced particle swarm optimization algorithm (HEPSO) to optimize the parameters, including [Formula: see text], [Formula: see text], ɛ and [Formula: see text], of SMC (HEPSO-SMC). The HEPSO integrates three strategies: adaptive inertia weightings (AIW), unified factor enhancement (UFE), and global optimal particle training (GOPT). The HEPSO is validated by simulation with CEC2022 which contains twelve benchmark functions, and the results show that the HEPSO is superior to the other variants of the PSO algorithm in terms of convergence speed and accuracy. The HEPSO-SMC is used as a 2-jointed manipulator for simulation verification. The simulation results, which are compared to PSO-SMC, IPSO-SMC, and UPS-SMC, are shown to illustrate the effectiveness and robustness of the HEPSO-SMC.

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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