使用PID控制器与Kookaburra和Red Panda优化算法的直流电机控制系统的性能改进
G Saravanan1, C Pazhanimuthu1, Palanichamy Naveen2
1Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407, India.
Scientific reports
|June 6, 2025
概括
新的元启发算法,库卡布拉优化算法 (KOA) 和红熊猫优化算法 (RPOA),被用来调整用于直流电机转速控制的比例积分导数 (PID) 控制器. KOA表现出优越的上升时间和带宽,而RPOA实现了较低的集成时间绝对误差 (ITAE).
科学领域:
- 控制系统工程 控制系统工程
- 计算智能是一种计算智能.
- 机器人和自动化 机器人和自动化
背景情况:
- 精确控制直流电机的转速对于工业应用至关重要.
- 比例积分导数 (PID) 控制器通常用于直流电机的转速控制.
- 调整PID控制器收益是具有挑战性的,特别是在复杂的系统中,通常需要元启发算法.
研究的目的:
- 研究库卡布拉优化算法 (KOA) 和红熊猫优化算法 (RPOA) 在直流电机转速控制系统中调整PID控制器收益的有效性.
- 为了比较KOA和RPOA在时间和频率响应,稳定性和融合特征方面的性能.
主要方法:
- 这项研究应用了KOA和RPOA,基于群集智能的元启发算法,以优化DC电机转速控制系统的PID控制器收益.
- 使用整合时间绝对误差 (ITAE) 目标函数来评估性能.
- 进行了时间响应 (上升时间,沉降时间) 和频率响应 (带宽) 分析.
- 在从±20%到±50%的参数变化下评估了强度.
主要成果:
- 与RPOA相比,KOA实现了更快的上升时间 (9.2-12.8%的改进) 和更大的带宽 (15-16.4%的改进).
- RPOA产生了较低的积分时间绝对误差 (ITAE).
- 两种算法都在第70次代时表现出接近,时间复杂度相似.
- 稳定性分析表明,在参数变化下,系统响应得到了改善.
结论:
- KOA 和 RPOA 是有效的元启发算法,用于在直流电机转速控制中调整 PID 控制器.
- KOA在动态性能指标上表现出色,例如上升时间和带宽.
- RPOA在最大限度地减少积分时间绝对误差 (ITAE) 方面表现出卓越的性能.
- 这两种算法都为直流电机速度控制应用提供了强大的解决方案.
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