Journal Article

Research on Optimization of Diesel Engine Speed Control Based on UKF-Filtered Data and PSO Fuzzy PID Control

Lin, Luchen, Wang, Nan, Chen, Zhenghong, Gu, Shuo, Fu, Jun, Wu, Lei

2025

Overview

With the continuous development of industrial automation, diesel engines play an increasingly important role in various types of construction machinery and power generation equipment. Improving the dynamic and static performance of the speed control system of single-cylinder diesel engines can not only significantly improve the efficiency of the equipment, but also effectively reduce energy consumption and emissions. Particle swarm optimization (PSO) fuzzy PID control algorithms have been widely used in many complex engineering problems due to their powerful global optimization capability and excellent adaptability. Currently, PSO-based fuzzy PID control research mainly integrates hybrid algorithmic strategies to avoid the local optimum problem, and lacks optimization of the dynamic noise suppression of the input error and the rate of change of the error. This makes the algorithm susceptible to the coupling of the system uncertainty and measurement disturbances during the parameter optimization process, leading to performance degradation. For this reason, this study proposes a new framework based on the synergistic optimization of the untraceable Kalman filter (UKF) and PSO fuzzy PID control for the speed control system of a single-cylinder diesel engine. A PSO-optimized fuzzy PID controller is designed by obtaining accurate speed estimation data using the UKF. The PSO is capable of quickly adjusting the fuzzy PID parameters so as to effectively alleviate the nonlinearity and uncertainty problems during the operation of diesel engines. By establishing a Matlab/Simulink simulation model, the diesel engine speed step response experiments (i.e., startup experiments) and load mutation experiments were carried out, and the measurement noise and process noise were imposed. The simulation results show that the optimized diesel engine speed control system is able to reduce the overshoot by 76%, shorten the regulation time by 58%, and improve the noise reduction by 25% compared with the conventional PID control. Compared with the PSO fuzzy PID control algorithm without UKF noise reduction, the optimized scheme reduces the overshoot by 20%, shortens the regulation time by 48%, and improves the noise reduction effect by 23%. The results show that the PSO fuzzy PID control method with integrated UKF has superior control performance in terms of system stability and accuracy. The algorithm significantly improves the responsiveness and stability of diesel engine speed, achieves better control effect in the optimization of diesel engine speed control, and provides a useful reference for the optimization of other diesel engine control systems. In addition, this study establishes the GT-POWER model of a 168 F single-cylinder diesel engine, and compares the cylinder pressure and fuel consumption under four operating conditions through bench tests to ensure the physical reasonableness of the kinetic input parameters and avoid algorithmic optimization on the distorted front-end model.

• Publisher: MDPI AG
• Subject: Accuracy / Adaptability / Agricultural production / Algorithms / Analysis / Automation / China / Construction equipment / Control methods / Control systems / Control theory / Controllers / Diesel / Diesel engines / Diesel motor / Efficiency / Emission standards / Energy conservation / Energy consumption / Engine control / Engine cylinders / Engines / Farm machinery / Fuzzy logic / Global optimization / Heat / Internal combustion engine industry / Kalman filters / Load / Mathematical optimization / Noise control / Noise measurement / Noise reduction / Nonlinear systems / Optimization / Parameters / Particle swarm optimization / Performance degradation / Proportional integral derivative / Simulation models / Speed control / Step response / Systems stability / Uncertainty