In this work, we aim to investigate the problem of steering actuator fault diagnosis for automated vehicles based on the approach of model-based Support Vector Machine (SVM) classification subject to unbalanced training datasets. The system model is utilized to generate the residual signal as the training data and the data-based algorithm of SVM classification is employed to diagnose the fault. Due to the phenomena of data unbalance in the data-driven method, an undersampling procedure with the approach of Linear Discriminant Analysis (LDA) and a threshold adjustment using the algorithm of Grey Wolf Optimizer (GWO) are proposed to modify and improve the performance of classification and fault diagnosis. Various comparisons are carried out based on widely-used datasets. The comparison results show that the proposed algorithm has superiority on the classification over existing methods. Experimental results and comparisons of an automated vehicle illustrate the effectiveness of the proposed algorithm on the steering actuator fault diagnosis.

As a typical example of cyber-physical systems (CPSs), intelligent vehicles are receiving increasing attention, and the obstacle avoidance problem for such vehicles has become a hot topic of discussion. This paper presents a simultaneous trajectory planning and tracking controller for use under cruise conditions based on a model predictive control (MPC) approach to address obstacle avoidance for an intelligent vehicle. The reference trajectory is parameterized as a cubic function in time and is determined by the lateral position and velocity of the intelligent vehicle and the velocity and yaw angle of the obstacle vehicle at the start point of the lane change maneuver. Then, the control sequence for the vehicle is incorporated into the expression for the reference trajectory that is used in the MPC optimization problem by treating the lateral velocity of the intelligent vehicle at the end point of the lane change as an intermediate variable. In this way, trajectory planning and tracking are both captured in a single MPC optimization problem. To evaluate the effectiveness of the proposed simultaneous trajectory planning and tracking approach, joint veDYNA-Simulink simulations were conducted in the unconstrained and constrained cases under leftward and rightward lane change conditions. The results illustrate that the proposed MPC-based simultaneous trajectory planning and tracking approach achieves acceptable obstacle avoidance performance for an intelligent vehicle.

In this paper, we aim to investigate the path following control problem for four-wheel-independent-drive electric vehicles (4WID-EVs) with consideration of modeling error and complex driving scenario. In order to guarantee operational safety and performance of robustness, a super-twisting secondorder sliding mode (SOSM) control strategy is employed to suppress the heavy chattering problem existing in the traditional sliding mode control (SMC). To degrade the lumped disturbance, a nonlinear disturbance observer (NDOB) technique is proposed to estimate the disturbance and compensate the modeling error. Then, a composite control strategy which combines the SMC algorithm and NDOB is proposed base on stability analysis of the corresponding closed-loop system. Finally, simulation results and comparisons show that the super-twisting SOSM control algorithm with the NDOB technique can achieve better tracking performance and suppress the chattering.

Recent advances in attack/anomaly detection and resilience strategies for connected and automated vehicles (CAVs) are reviewed from vehicle dynamics and control perspective. Compared to traditional vehicles, CAVs are featured in the increasing number of perception sensors, advanced intra-vehicle communication technologies, capabilities of driving automation and connectivity between single vehicles. These features bring about safety issues which are not encountered in traditional vehicle systems. One main type of these issues is the attack or anomaly launched onto the perception sensors and the communication channels. With such a consideration, this survey summarizes and reviews the existing results on attack/anomaly detection and resilience of CAVs in control frameworks. This paper reviews the results according to the positions at which the attacks/anomalies occur. These positions are divided into three categories, namely, intra-vehicle communication network, perception sensors and inter-vehicle communication network. From this perspective, the recent attack/anomaly detection and resilience results are reviewed according to different positions attacked. After reviewing existing results, some potential research directions and challenges are identified.

In this paper, we investigate the tuning problem of digital proportional-integral-derivative (PID) parameters for a DC motor controlled via the controller area network (CAN). Firstly, the DC motor is modeled and the parameters are identified with experimental data. By studying the CAN network characteristics, we obtain the CAN-induced delays with respect to the load rate and the priorities. Then, considering the system model, the network properties, and the digital PID controller, the tuning problem of PID parameters for CAN-based DC motor are transformed into a design problem of a static-output-feedback (SOF) controller for a time-delayed system. A sufficient condition is adopted to guarantee the stability of the time-varying delay system. In order to tune the controller gains, we propose an optimization method based on the particle swarm optimization (PSO) algorithm and the linear-quadratic-regulator (LQR) technique. The sufficient stability condition is incorporated in the modified PSO algorithm. Finally, experimental results validate the advantages of the proposed tuning approach.

Vehicle platoon control based on vehicle-to-vehicle (V2V) communication is one of the promising technologies to improve the performance of transportation systems. This paper presents a distributed controller to optimize a vehicle platoon’s fuel consumption by combining the switching feedback control and economic model predictive control (EMPC) methods. The closed-loop dynamics involving switching feedback gains with the constant time headway (CTH) policy are established firstly, and the multiple-predecessor following (MPF) communication topology is considered. In order to obtain the economy optimal feedback gain, we design a local optimal control problem for each vehicle, based on which the average dwell time is defined and the distributed EMPC algorithm is designed. Based on linear matrix inequalities (LMIs) and the Lyapunov theorem, the feedback gain selection method and the lower bound for average dwell time that guarantees asymptotic stability are analyzed rigorously. Then a modified algorithm that can ensure string stability is designed. Numerical simulations show a maximum of 6.84% fuel benefit compared with pure tracking-oriented methods.