基于GWO的多级算法用于PMDC电机参数估计
Adam Pawlowski1, Maciej Ciezkowski1, Slawomir Romaniuk1
1Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Bialystok University of Technology, ul. Wiejska 45D, 15-351 Bialystok, Poland.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究提出了一种用于调整轮式移动机器人的电机控制器的新方法. 通过估计永久磁直流 (PMDC) 电机的参数范围,优化算法变得更加有效和准确.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 电气工程 电气工程
背景情况:
- 精确的电机控制器调整对于移动机器人的性能至关重要.
- 恒磁直流 (PMDC) 电机参数的识别是精确控制的关键.
- 优化算法,像遗传算法一样,越来越多地用于参数识别,但如果没有适当的搜索范围,它可能是低效的.
研究的目的:
- 引入一种有效的方法来确定永磁直流 (PMDC) 电机参数.
- 为了应对因广泛的搜索范围而导致优化算法中低效的参数识别的挑战.
- 为了提高生物灵感优化算法的时间效率和解决方案寻找能力,用于电机参数估计.
主要方法:
- 开发一种新的方法,用于PMDC电机的参数搜索范围的初始估计.
- 利用基于优化的技术,特别是遗传算法,进行参数识别.
- 实施一种改进搜索范围的方法,以加快优化过程.
主要成果:
- 提出的方法成功估计了PMDC电机所需的参数范围.
- 参数范围的初始估计显著减少了遗传算法的计算时间.
- 该方法提高了机器人电机控制器的参数识别的效率和有效性.
结论:
- 开发的估计PMDC电机参数范围的方法优化了生物灵感算法的性能.
- 这种技术导致更精确的控制器调整和改进的移动机器人动态.
- 这些发现有助于更高效和有效的机器人设计和控制系统开发.
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