This paper investigates the path-tracking control issue for autonomous ground vehicles with the integral sliding mode control (ISMC) considering the transient performance improvement. The path-tracking control is converted into the yaw stabilization problem, where the sideslip-angle compensation is adopted to reduce the steady-state errors, and then the yaw-rate reference is generated for the path-tracking purpose. The lateral velocity and roll angle are estimated with the measurement of the yaw rate and roll rate. Three contributions have been made in this paper: 1) To enhance the estimation accuracy for the vehicle states in the presence of the parametric uncertainties caused by the lateral and roll dynamics, a robust extended Kalman filter is proposed based on the minimum model error algorithm; 2) An improved adaptive radial basis function neural network (RBFNN) considering the approximation error adaptation is developed to compensate for the uncertainties caused by the vertical motion; 3) The RBFNN and composite nonlinear feedback (CNF)-based ISMC is developed to achieve the yaw stabilization and enhance the transient tracking performance considering the input saturation of the front steering angle. The overall stability is proved with Lyapunov function. Finally, the superiority of the developed control strategy is verified by comparing with the traditional CNF with high-fidelity CarSim-Matlab simulations.

Path‐tracking and lane‐keeping efficiency of driverless cars remain critical characteristics of the efficient and safe deployment of such vehicles in future intelligent transportation systems. This study introduces a robust type‐3 (T3) fuzzy controller implementation for the path‐tracking task of driverless cars during critical driving conditions and subject to exogenous disturbances. Unlike many existing control paradigms, the proposed scheme is independent of the parameter information and assumes the system dynamics are unknown and non‐linear. Control inputs are constructed to improve robustness by eliminating the error bounds while ensuring stability by leveraging the Lyapunov stability theorem and Barbalat's lemma. Also, a predicate scheme based on non‐linear predictive control technique is introduced to enhance the lateral displacement. Based on the obtained results, the schemed controller exhibits competitive effectiveness in path‐tracking tasks, and strong efficiency under various road conditions, parametric uncertainties, and unknown disturbances.

Efficient, safe, and comfortable electric vehicles (EVs) are essential for the creation of a sustainable transport system. Distributed-driven EVs, which often use in-wheel motors (IWMs), have many benefits with respect to size (compactness), controllability, and efficiency. However, the vibration of IWMs is a particularly important factor for both passengers and drivers, and it is, therefore, crucial for the successful commercialization of distributed-driven EVs. This article provides a comprehensive literature review and state-of-the-art vibration-source analysis and mitigation methods in IWMs. First, selection criteria are given for IWMs, and a multidimensional comparison for several motor types is provided. The IWM vibration sources are then divided into internally and externally induced vibration sources and discussed in detail. Next, vibration reduction methods, which include motor-structure optimization, motor controller, and additional control components, are reviewed. Emerging research trends and an outlook for future improvement aims are summarized at the end of this article. This article can provide useful information for researchers who are interested in the application and vibration mitigation of IWMs or similar topics.