模糊推理系统启用神经网络前补偿,用于DC伺服电机的位置跳跃控制
Zhiwen Huang1, Yuting Yan1, Yidan Zhu2
1School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Scientific reports
|September 6, 2024
概括
这项研究引入了一种新的模糊推理系统 (FIS),启用了人工神经网络 (ANN) 控制方法,以提高直流伺服电机的性能. 该方法提高了位置控制精度和动态响应,优于传统方法.
科学领域:
- 控制系统工程 控制系统工程
- 自动化中的人工智能
- 机器人和机械电子学
背景情况:
- 直流 (DC) 伺服电机在自动化中至关重要,但在动态性能和稳定状态准确性方面面临挑战.
- 传统的控制方法,比如比例积分导数 (PID) 控制器,经常与复杂的系统动态和外部干扰作斗争.
- 现有的人工智能 (AI) 方法可能需要广泛的培训或缺乏适应实时系统变化的能力.
研究的目的:
- 开发DC伺服电机的先进控制策略,以提高动态响应和稳定状态准确性.
- 提出一种新的模糊推理系统 (FIS) 启用人工神经网络 (ANN) 的前补偿控制方法.
- 为了提高直流伺服电机系统中位置跳跃控制的精度和适应性.
主要方法:
- 一个基线比例-积分-导数 (PID) 控制器产生主要控制信号.
- 一个人工神经网络 (ANN) 识别器学习直流伺服电机系统的逆动力学,以进行在线补偿.
- 一个模糊推理系统 (FIS) 决策器根据跟踪和建模错误适应性地调整补偿,减轻干扰.
主要成果:
- 在DC伺服电机测试台上的实验验证表明,与其他控制方法相比,性能优越.
- 拟议的支持FIS的ANN前补偿控制实现了显著降低超标和更快的响应时间.
- 该方法在跟踪步骤和方形信号方面表现出更高的精度,证实了其有效性.
结论:
- 拟议的模糊推理系统 (FIS) 启用人工神经网络 (ANN) 的前补偿控制方法对于直流伺服电机位置控制是有效的.
- 这种先进的控制策略提供了更好的动态性能,稳定状态精度和对干扰的稳定性.
- 整合FIS和ANN为复杂的伺服系统提供了强大的自适应控制解决方案.
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