A hybrid fuzzy and proportional-integral-derivative (PID) controller is proposed for roll angle handling of a three-axle truck with an active air suspension system. The conventional truck suspension system has four air springs for the rear wheels and two leaf springs for the front wheels, which cannot properly control the pitch angle, and here in this study is upgraded into front air springs. Therefore in the full air suspension system, the pitch angle is controlled by the active suspension system. Roll reduction of a heavy vehicle can improve the ride comfort and rollover tendency of the truck, simultaneously. The relation of air spring pressures and vehicle dynamics is developed in a simple and accurate model. Using this comprehensive model, it is possible to control the variables of vehicle dynamics such as roll, pitch, and height of the truck. The truck air suspension system is examined in step steering, fishhook, and asymmetric rough road (types E and G power spectral density [PSD] road) tests. The fuzzy input is a normalized roll angle and the output is the normalized mass flow rate (of the air springs). Both of the fuzzy input and output have nine membership functions (MFs), which have optimized with the genetic algorithm (GA) method. The optimization cost function is a combination of maximum and integral of the absolute roll angle of the truck sprung mass. Besides, the PID controller is tuned by the Ziegler-Nichols method at the first stage and optimized by the GA method. The results show that the optimized fuzzy controller has good roll performance in a different test; however, the simple PID addition to the fuzzy controller can improve vehicle comfort and stability.
