In today's automotive world, the driving experience hinges significantly on the ride quality and handling of vehicles. Furthermore, the vehicles’ control and stability are the important aspects of safety as well. This study focusses on improved roll stability of the vehicle, which is one of the key factors for improved ride comfort, control and handling characteristics. In general, vehicles are equipped with passive Anti-roll bars (ARB) to reduce roll motion during cornering or riding on uneven roads. However passive ARB underperforms in the critical conditions such as high speed cornering thus deteriorates the ride comfort and handling. Hence, the active roll bar is introduced for better performance. In this seminar, the active ARB control for independent suspension that enhances the vehicle control and handling will be discussed. In the first part of the seminar, the existing ARB control strategies are thoroughly investigated and based on the inference a hybrid control strategy is developed. In the second part of the seminar, we will introduce a modified control strategy with accurate modelling of system dynamics for improved performance. Subsequently, the simulation findings (steering angle, vehicle velocity, and actuator torque) are utilized to train a neural network (NN) that outputs the required torque to be given to the actuator in the developed experimental setup. In the final part of the seminar, the efficacy of the developed control strategy is demonstrated using CarSim® and the Matlab®/Simulink co-simulation environment. Furthermore, the experimental studies involving demonstration of designed and developed ARB with actuator feedback control are presented.
Keywords: Anti-roll bar (ARB); Control Algorithm; Neural Network (NN); Roll stability; Feedback control.