Abstract
Both yaw moment control and steering control of a vehicle play an important role in its stability especially in complicated maneuver on high speeds. Controlling these two parameters depends on two unique system inputs; driving/braking forces and front/rear steering angle. Various studies have proposed systems for controlling these two parameters to reach an optimum performance, some of which has even used fuzzy logic controllers.solely in such safety critical systems. However, these controllers had depended only on expert's knowledge, that can't be relied on solely in such safety critical systems. In the proposed study, we introduce an adaptive neuro-fuzzy control system, where an Artificial Neural Network is used to construct a fuzzy control system through learning from the optimum control values. This approach allows benefiting from the learning and autoadaption capability of neural networks and the smooth controlling performance that fuzzy logic controller offers. Simulations results show the effectiveness of the proposed controller for improving vehicle yaw stability. Not only did it reduce the error but also it yielded a smoother performance than that of the other systems.
KEYWORDS: yaw moment control, steering control, intelligent vehicle control, fuzzy logic, adaptive neuro-fuzzy