由于船舶航行环境复杂且多变，动态切换功率难以满足负荷状态的需求功率，导致负荷输出与期望值不一致，因此，提出船舶柴油机动力装置负荷切换状态自适应控制方法。构建NARX神经网络负荷预测模型，判断从高负荷状态切换到低负荷状态的需求功率；引入自适应权重混合PSO算法，根据当前和预测的负荷状态，设计自适应预测PID控制器，实现对船舶柴油机动力装置负荷状态变化的响应。实验结果表明，在所研究方法应用下，误差随时间累积的绝对值积分相对较小，可以实现不同负荷状态切换场景下，负荷输出与期望值的紧密一致。
Due to the complex and variable ship navigation environment, the dynamic switching power is difficult to meet the demand power of the load state, which leads to the inconsistency between the load output and the expected value, therefore, the adaptive control method of the load switching state of ship's diesel engine power unit is proposed.The NARX neural network load prediction model is constructed to judge the demand power for switching from a high load state to a low load state; the adaptive weight mixing PSO algorithm is introduced, and the adaptive prediction PID controller is designed according to the current and predicted load states to achieve the response to the change of the ship's diesel engine power unit load state.The experimental results show that under the application of the studied method, the absolute value integral of the error accumulated over time is relatively small, and the close agreement between the load output and the desired value can be achieved under different load state switching scenarios.
2025,47(20): 146-150 收稿日期：2025-4-16
DOI：10.3404/j.issn.1672-7649.2025.20.022
分类号：U664.121；TP25.81
基金项目：2024年度河南省自然科学基金项目（面上科学基金项目）（242300420038）
作者简介：李沙沙(1992-),女,硕士,讲师,研究方向为电力系统、故障诊断、控制算法
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