Integrated control of vehicle stability by nonlinear observer-based exponential-like sliding mode neural network system

Dynamic stability is critical to achieve the safety of the cars, particularly during emergency maneuvers. Coordinated control algorithms are suggestive of the enhanced safety and stability of a vehicle. Hence, a novel adaptive robust multi-input control framework is developed using the combination of direct yaw moment (DYC) and active front steering (AFS). The dynamics of the steering system mechanism is included for the reliability of the proposed control scheme. The proposed controller is developed according to the approximation capacity of the radial basis function (RBF) neural network system. The adaptation laws are derived based on the Lyapunov stability theory. Additionally, the proposed integrated control paradigm contains a state observer and the sliding surface of the tracking errors converges to the asymptotic stability condition through the design of a smooth exponential reaching law. The effectiveness of the proposed control scheme is compared to a high-performance optimal robust control technique and open-loop system. In order to assess the robustness of the proposed algorithm, structured and unstructured uncertainties were also incorporated in terms of the parametric uncertainties such as the tire cornering stiffness and cross-wind gust disturbance. The results obtained for different maneuvers reveal that the proposed controller is successful to improve the handling performance and to ensure the stability of the vehicle when compared to the previously reported methods.