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在hysteresis非线性条件下,基于预测的单动气动气快速力控制
Hongliang Hua1, Jing Zhang2, Che Zhao2
1School of Mechanical Engineering, Changzhou Institute of Technology, Changzhou, Jiangsu 213032, PR China; School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, PR China.
ISA transactions
|January 28, 2025
概括
本研究介绍了一种基于神经网络预测的比例整微差分 (NNP-PID) 控制器,用于控制气动系统中的歇斯底里. NNP-PID显著提高了单作用式气动气瓶的控制精度和速度.
科学领域:
- 控制系统工程 控制系统工程
- 机器人和自动化 机器人和自动化
- 人工智能的人工智能
背景情况:
- 气动系统中的歇斯底里降低了性能和可靠性.
- 对于工业应用来说,精确控制单动式气动气瓶 (SAPC) 的驱动力至关重要.
- 现有的控制方法很难有效地补偿歇斯底里.
研究的目的:
- 开发一种快速而准确的方法来控制SAPC的驱动力,特别是解决hysteresis.
- 引入和评估基于神经网络预测的比例整数差异 (NNP-PID) 控制策略.
- 为了证明NNP-PID控制器的实时适用性和性能改进.
主要方法:
- 基于神经网络预测的比例积分差异 (NNP-PID) 控制策略的实施.
- 开发一个神经网络预测模型,用于快速力量控制.
- 在SAPC上进行控制实验,以评估NNP-PID性能与传统PID控制器相比.
- 评估8位微控制器上的计算成本,以实时实现可行性.
主要成果:
- NNP-PID模型在8位微控制器上实现了1.22毫秒的计算成本,满足了实时需求.
- 与传统的PID相比,NNP-PID控制器显示出显著的改进.
- 观察到控制超标 (17.5%),上升时间 (65.9%),结算时间 (19.8%) 和稳定状态错误 (46.4%) 的减少.
结论:
- 该NNP-PID控制策略提供了一个快速而准确的解决方案,用于控制动力在SAPCs与歇斯底里.
- 由于其计算成本低,开发的系统适合实时工业应用.
- 与传统的PID控制器相比,NNP-PID提供了更高的性能,提高了系统的效率和精度.
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