基于三层BP神经网络的液压气输入-输出特征曲线的预测
Wei Cai1,2,3, Yirui Zhang2, Jianxin Zhang2
1State Key Laboratory of Crane Technology, Yanshan University, Qinhuangdao 066004, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
一个新的神经网络模型通过预测活塞杆位移来提高液压的准确性. 这种方法尽量减少错误,并优化系统响应时间,以提高运营效率.
科学领域:
- 机械工程 机械工程
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 液压气在各种工业应用中至关重要,但它们的位移控制精度通常受到流体压缩性和泄漏等因素的限制.
- 传统的方法很难解释影响活塞杆运动的众多参数的复杂相互作用.
研究的目的:
- 开发一种先进的模型,以高精度预测液压气的活塞杆位移.
- 提高液压系统的实时控制和运行效率.
主要方法:
- 建立了内部和外部液压气泄漏的数学模型,考虑到压缩性诱导的流量损失.
- 开发了一种具有非线性自适应减重机制的三层反向传播 (BP) 神经网络合奏模型.
- 利用代训练和测试来确定最佳重量和偏差参数,以实时预测位移.
主要成果:
- 拟议的模型实现了0.5491μm的最大预测位移误差.
- 该模型的最大运行时间为27.82毫秒,可实现实时预测.
- 成功地将方向开关和流体缓冲的允许时间延长到74.57毫秒.
结论:
- 神经网络组合模型显著提高了液压气中位移控制的准确性.
- 积极的位移预测有效地弥补了系统响应延迟,提高了整体运营效率.
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