PID控制算法基于多层次增强的虫优化器和反向传播神经网络,用于直流电机控制
Weibin Kong1, Haonan Zhang1, Xiaofang Yang1
1School of Information Engineering, Research Center of Photoelectric and Information Technology, Yancheng Institute of Technology, Yancheng, 224000, Jiangsu, China.
Scientific reports
|November 16, 2024
概括
这项研究引入了一种增强的虫优化器 (EDBO) 和反向传播神经网络 (BPNN),用于自适应的比例整合导数 (PID) 控制. 这种新的方法显著提高了电机控制应用中的系统稳定性和稳定性.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 传统的比例积分导数 (PID) 控制器与非线性和时间变化的系统扎.
- 原来的虫优化器 (DBO) 在勘探,勘探-开发平衡和全球搜索精度方面存在局限性.
研究的目的:
- 提出一种新的自适应PID控制算法,使用增强的虫优化器 (EDBO) 和反向传播神经网络 (BPNN).
- 提高控制系统的性能,稳定性和稳定性,特别是在电机应用中.
主要方法:
- 将以优势为导向的机制和正弦学习因子纳入甲虫优化器,以加强勘探和平衡利用.
- 实施动态螺旋搜索策略和适应性干扰,以提高搜索精度和全球能力.
- 使用反向传播神经网络 (BPNN) 微调 PID 和网络参数以建模非线性动态.
主要成果:
- 在简化运动实验中实现了最低的超越率 (0.5%) 和最短的响应时间 (0.012秒).
- 在五次直流电机测试中表现出高性能,超速0.7%和0.0010秒的响应时间.
- 验证了系统强度,稳定性和参数优化能力的改进.
结论:
- 基于EDBO和BPNN的拟议的自适应PID控制算法,与传统方法相比,提供了更高的性能.
- 增强的优化技术有效地解决了原始DBO的局限性,从而导致更好的控制系统结果.
- 该算法显示了对于需要精确和强大的非线性系统控制的应用程序的巨大潜力.
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