Research on the global energy optimization of multi-source and multi-actuator hydraulic systems based on dynamic programming and improved adaptive genetic algorithm

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions Pub Date : 2025-06-11 DOI:10.1016/j.isatra.2025.06.010

Yuhang Zhong , Wenting Chen , Zihao Chen , Guanyu Zhai , Chao Ai , Gexin Chen

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引用次数: 0

Abstract

Multi-source and multi-actuator hydraulic systems (MSAHSs) are widely used in high-power energy transmission and construction machinery. However, individual control of each component without considering the overall power matching leads the system to the low-efficiency zone, results in environmental pollution and huge economic loss. Therefore, it is highly desirable to find a way of obtaining energy-saving green MSAHSs. In this paper, the power consumption model of closed MSAHSs is established firstly to analyze theoretical factors affecting the component efficiency and find that the hydraulic pressure is the key factor. On this basis, a multi-algorithm integration global power matching method is then proposed, which consist of back propagation (BP) neural network, dynamic programming (DP) and improved adaptive genetic algorithm (IAGA). BP is used to construct efficiency prediction models for power elements (pumps, motors and engines) respectively, DP is used for elements’ high efficiency zone preliminary search, and IAGA is used to realize the global power matching of the multiple power units with energy conversion and transfer finally through optimal control parameters precise searching. Experiment is conducted on the closed MSAHS in a hydraulic fracturing vehicle. Results demonstrate that the MSAHS applied with multi-algorithm integration method improves the overall efficiency to a highest fuel savings of 35.5 % under normal conditions compared with local power matching control.

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基于动态规划和改进自适应遗传算法的多源多作动器液压系统全局能量优化研究。

多源多致动器液压系统广泛应用于大功率能量传输和工程机械中。但是，不考虑整体功率匹配而单独控制各部件，使系统进入低效率区，造成环境污染和巨大的经济损失。因此，寻找一种获得节能绿色msahs的方法是非常必要的。本文首先建立了闭式msahs的功耗模型，分析了影响元件效率的理论因素，发现液压是影响元件效率的关键因素。在此基础上，提出了一种由BP神经网络、动态规划（DP）和改进自适应遗传算法（IAGA）组成的多算法集成全局功率匹配方法。利用BP分别构建动力单元（泵、电机和发动机）的效率预测模型，利用DP对各单元的高效区进行初步搜索，利用IAGA通过最优控制参数的精确搜索，最终实现多动力单元的能量转换和传递的全局功率匹配。在水力压裂车上对闭式MSAHS进行了试验。结果表明，与局部功率匹配控制相比，采用多算法集成方法的MSAHS在正常情况下可将整体效率提高到35.5% %的最高节油率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ISA transactions 工程技术-工程：综合

CiteScore

11.70

自引率

12.30%

发文量

824

审稿时长

4.4 months

期刊介绍： ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.