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.

动态稳定性对于确保汽车的安全至关重要，特别是在紧急情况下。协调控制算法暗示了车辆安全性和稳定性的提高。因此，使用直接横摆力矩（DYC）和主动前转向（AFS）的组合，开发了一种新颖的自适应鲁棒多输入控制框架。为了确保所提出的控制方案的可靠性，包括了转向系统机构的动力学特性。所提出的控制器是根据径向基函数（RBF）神经网络系统的逼近能力而开发的。自适应律是基于Lyapunov稳定性理论得出的。此外，所提出的集成控制范式包含一个状态观测器，并且通过设计光滑指数到达定律，跟踪误差的滑动表面收敛到渐近稳定条件。将所提出的控制方案的有效性与高性能最优鲁棒控制技术和开环系统进行了比较。为了评估所提出算法的鲁棒性，还根据参数不确定性（例如轮胎转弯刚度和侧风阵风干扰）并入了结构化和非结构化不确定性。与先前报道的方法相比，针对不同操纵获得的结果表明，所提出的控制器能够成功地改善操纵性能并确保车辆的稳定性。