An RBFNN-based constraint control algorithm for electric vehicle AFS plus DYC system with state delays and uncertain parameters

A novel multi-input multi-output adaptive control strategy applied to the combined active front steering (AFS) and direct yaw moment control (DYC) system is proposed in this paper. Studies have been conducted to show the potential for electric vehicles to be affected by time delay, which may appear in states. Aiming to address the state delay, radial basis function neural network (RBFNN) and Lyapunov-Krakovskii functional-based AFS and DYC controllers are constructed, which also can process the parameter uncertainties due to the powerful ability of RBFNN to approximate the unknown terms. Meanwhile, the controller is designed to incorporate the barrier Lyapunov function so that the state variables under control, including the sideslip angle and yaw rate, remain in their respective stable ranges while accurately tracking the corresponding desired values. Co-simulations utilizing Simulink and CarSim are implemented for verifying the effectiveness of the proposed control scheme and comparing it with the traditional control scheme.