Table of fuzzy rules.

Given the nonlinear and time-varying characteristics of diesel engine speed control, a conventional proportional integral derivative (PID) controller is inadequate for addressing the lag or overshoot in the system response, and it struggles to adapt to complex dynamic changes under load. This study proposes a fuzzy proportional integral derivative (FPID) control,which is based on an improved sparrow search algorithm(ISSA) with the aim of enhancing the system’s adaptability. By refining the algorithm to augment its parameter control capabilities and employing test functions for experimental comparisons, the improved algorithm exhibited accelerated convergence and increased accuracy. The improved sparrow search algorithm is applied to two controllers for experimental comparison, and the results indicate that, in contrast to the traditional PID control algorithm, the FPID control algorithm reduces the adjustment time by 1.4 s and decreases the overshoot by 6.8% when the speed is adjusted to 2000 revolutions per minute (RPM). The duration for speed fluctuation stabilization under load changes of 8 and 10 is decreased by 18% and 30%, respectively, and the fluctuation deviation of the speed is reduced by 7% and 12%, respectively. Consequently, the implementation of FPID parameters tuned by the improved sparrow algorithm provides robust support for the stable operation of a diesel engine during speed fluctuations.

鉴于柴油机转速控制系统具备非线性与时变特性，传统比例积分微分（Proportional Integral Derivative, PID）控制器难以应对系统响应的滞后与超调问题，且无法适配负载工况下的复杂动态变化。本研究提出一种基于改进麻雀搜索算法（Improved Sparrow Search Algorithm, ISSA）的模糊比例积分微分（Fuzzy Proportional Integral Derivative, FPID）控制方法，以提升系统的自适应能力。通过优化算法以增强其参数调控能力，并结合测试函数开展对比实验，改进后的算法收敛速度更快、精度更高。将改进麻雀搜索算法应用于两种控制器的对比实验，结果显示：相较于传统PID控制算法，当转速调整至2000转每分钟（Revolutions Per Minute, RPM）时，FPID控制算法的调节时间缩短1.4秒，超调量降低6.8%。当负载变化量为8和10时，转速波动稳定所需时长分别缩短18%与30%，转速波动偏差分别降低7%与12%。综上，采用改进麻雀搜索算法整定的FPID参数，可为柴油机转速波动工况下的稳定运行提供可靠保障。