优化电力消耗和位置控制在一个电动液压系统与气绕道和NN-MPC优化
Omar Hesham Khedr1,2, Mohammed Ibrahim Awad3, Affaf Khamis Al-Oufy4,5
1Mechatronics Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt. 2002595@eng.asu.edu.eg.
Scientific reports
|January 23, 2024
概括
神经网络模型预测控制 (NN-MPC) 显著提高了电液系统的效率和位置控制,即使在不同的负载下. 这种创新方法通过优化流量控制的使用,可以节省高达9%的能源.
科学领域:
- 工程 工程师 工程师 工程师
- 控制系统 控制系统
- 能源效率 能源效率 能源效率
背景情况:
- 电液系统在各种行业中至关重要,但经常面临能源效率和精确位置控制方面的挑战.
- 像PID和标准MPC这样的现有控制策略在适应动态负载条件方面存在局限性.
- 优化能源消耗,特别是通过流量控制,是系统改进的一个关键领域.
研究的目的:
- 引入和比较评估电液系统的先进控制技术.
- 评估比例整数导数 (PID) 控制,模型预测控制 (MPC) 和神经网络模型预测控制 (NN-MPC) 的性能.
- 通过将传统系统与使用完全开放的流量控制 (FCV) 的拟议系统进行比较来量化节能.
主要方法:
- 对PID,MPC和NN-MPC控制策略的比较分析.
- 在多样化和变化的负载条件下的系统评估.
- 能源消耗分析比较传统设置与FCV集成系统.
主要成果:
- 在位置控制和能源效率方面,NN-MPC表现出卓越的性能,可稳定处理负载变化.
- 采用完全开放的流量控制 (FCV) 的拟议系统显示了显著的节能效果.
- 在优化的FCV策略下,在高负载水平下节省了高达9%的能源.
结论:
- NN-MPC是一种高效的控制策略,用于提高电液系统的性能和效率.
- 优化使用流量控制 (FCVs) 是一种可行的方法,可以在这些系统中大幅减少能源.
- 这些发现为开发更高效,更快速响应的电动液压驱动器提供了一条道路.
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