Nonlinear control of engine speed regulation using grey wolf optimizer for enhanced system stability and performance

Abstract

Accurate control of internal combustion engine speed, especially under variable load conditions, has always been a significant challenge in the automotive industry. Classical PID controllers often fail to effectively compensate for nonlinearities and environmental disturbances in spark ignition (SI) engines. To address this issue, we propose a method based on tuning PIDF controller parameters using the grey wolf optimizer (GWO) to enhance system stability and performance. This approach aims to reduce steady-state error, settling time, and overshoot. A mathematical model of the engine speed control system is developed, and the GWO algorithm is applied to optimize the PIDF gains. The performance of the GWO-based controller is then compared to other metaheuristic methods such as particle swarm optimization (PSO), differential evolution (DE), and cuckoo search (CS) algorithms through simulation. Simulation results demonstrate that the proposed GWO-based approach outperforms the alternatives by achieving better error reduction, improved stability, enhanced disturbance rejection, and faster response times.