PID controller optimized by grey wolf optimizer for semi-active vehicle suspension system

Modern automobiles have evolved from leaf and coil suspension in 1904 to electronic suspension in the 1980s, where the suspension system provides the user with easy control of the vehicle and makes the driver and passenger comfortable with the impact of particular road conditions. Control suspension systems have been extensively researched using smart artefacts to improve ride comfort and road holding. Suspension systems come in three types: passive, active, and semi-active. The semi-active suspension combines the benefits of both passive and active suspension for enhanced comfort, safety, and energy efficiency. Various controllers have been introduced, but the PID controller is the most common control algorithm used and has been universally applied in many industrial applications. The PID controller is favoured due to its affordability, simplicity in control structure and ease of maintenance. However, the drawback of this controller is finding the right parameter values, which is timeconsuming to achieve the system's optimal performance. Therefore, the grey wolf optimizer (GWO) is proposed to enhance the controller performance. GWO is a popular optimization algorithm that mimics grey wolfs' social structure and hunting tactics. This research investigates the advantages of this novel optimization algorithm in improving vehicle suspension systems.