This paper deals with the trajectory following control problem of a class of autonomous vehicles with parametric uncertainties, external disturbances, and over-actuated features. A novel adaptive hierarchical control framework is proposed to supervise the lateral motion of autonomous four-wheel independent drive electric vehicles. First, an adaptive sliding mode high-level control law with the linear matrix inequality-based switching surface is designed to produce a vector of front steering angle and external yaw moment, in which the uncertain term and the switching control gain are adaptively regulated by the fuzzy logic technique, to further moderate the chattering phenomenon, an adaptive boundary layer is introduced. Second, a pseudo-inverse low-level control allocation algorithm is presented to optimally allocate the external yaw moment via coordinating and reconstructing the tire longitudinal forces. Finally, numerical simulation and experimental results demonstrate that the proposed adaptive control approach has outstanding tracking performance.

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.

Owing to the fact that unmanned ground vehicles have the features of time-varying, parametric uncertainties and external disturbances, this paper mainly studies robust automatic steering control of unmanned ground vehicles. Firstly, a linear parameter varying lateral model for unmanned ground vehicle is constructed, in which the longitudinal velocity is represented by a polytope with finite vertices. Secondly, a robust gain scheduling automatic steering control scheme based on the linear matrix inequality technique is proposed to deal with the characteristics of time-varying and external disturbances. Finally, Simulation results based on Adams–Matlab joint platform using a nonlinear full vehicle model have demonstrated that the proposed control approach can simultaneously ensure the control accuracy and strong robustness of system.

As one of the typical applications of cyber-physical system (CPS), autonomous vehicles (AVs) are vulnerable to malicious disturbance from cyber-attacks while tracking the desired path. This paper focuses on CPS-based path tracking control problem of AVs under cyber-attacks. Firstly, the nonlinear state and measurement equations of AVs under cyber-attacks are established based on vehicle dynamics model. Secondly, to improve the robustness of AVs against cyber-attacks, sensor redundancy is introduced. A cyber-attack detection method is designed by using extended Kalman filter (EKF), and the computational complexity of the cyber-attack detection method is analyzed. In addition, sensor switching rules are developed to isolate the disturbance of cyber-attacks. Then, the control problem of the AVs is formulated based on model predictive control (MPC). Input-to-state stability (ISS) of the control system under cyber-attacks is established, and a link between the tolerable attack intensity and the detection thresholds is clearly revealed. Finally, the simulation results demonstrate the effectiveness of the proposed control strategy.

A deep investigation of amorphous V₂O₅–P₂O₅ powders for magnesium batteries communicates the vital properties to achieving the superior electrochemical performance at a 75 : 25 V₂O₅ : P₂O₅ molar ratio.