Optimizing Automotive Electronic Throttle Control with a Modified Grey Wolf Algorithm

Electronic Throttle Control (ETC) systems have become increasingly popular in the automotive industry due to their ability to improve fuel efficiency and vehicle performance. However, designing effective control strategies for these systems is challenging due to the non-linearities and external load affecting the throttle gate angle. This paper proposes a novel control approach for ETC systems using a modified Grey Wolf optimization algorithm combined with a fractional-order PID controller. The proposed approach is designed to handle the tuning of the controller's five parameters in the presence of many non-linearities and external disturbances. To better understand the effects of non-linearities, a comprehensive survey of ETC system control methods is presented, highlighting the challenges posed by non-linearities and external load. A mathematical model is developed to incorporate the effects of friction and limp home dual springs to understand the system's behavior. Modifications are made to the Grey Wolf optimizer to improve its convergence and ability to find the best solution. Simulation studies are conducted to compare the proposed approach with traditional PID and fractional-order PID controllers, demonstrating its superiority in terms of tracking performance and robustness to disturbances. The proposed approach has the potential to be applied to other control systems in the automotive industry and beyond © 2023 IEEE.