Connected automated vehicle (CAV) platoon control is becoming increasingly prevalent because of its unique advantages in reducing fuel consumption and improving traffic efficiency. A novel control framework for CAV platoon control is designed in this paper. First, a model predictive control (MPC)-based method is proposed to obtain the optimal velocity of the whole platoon, in which both reducing fuel consumption and improving transport efficiency are taken into account in the optimization process. Then, a distributed adaptive triple-step nonlinear control strategy is investigated from the perspective of multi-agent system control. The adaptive performance of the control strategy can guarantee string stability of the CAV platoon under the premise of the existence of dynamic uncertainties. Various simulation conditions with heterogeneous dynamic disturbances are designed to validate the proposed control strategy, and the results show that the proposed control strategy can be robust to dynamic disturbances while ensuring the string stability of the CAV platoon.

This paper investigates a triple-step approach-based fault-tolerant control (FTC) strategy for in-wheel motor electric ground vehicles, whose purpose is to preserve stability, improve handling with in-wheel motors and/or steering system faults. The proposed scheme is divided into two levels. The first level is the integrated vehicle motion control with model uncertainties, disturbances and the possible actuator faults, in which a triple-step nonlinear controller with on-line updating laws is designed. The second level is the driving reference signal generation, where the triple-step feedback law is used to provide a quantitative driving guidance and a residual signal with threshold for monitoring and evaluating drivers. The proposed FTC scheme not only achieves the vehicle safety but also the high-availability of the closed-loop driver-vehicle system. Simulation results show the effectiveness of the proposed scheme.

Automated manual transmission (AMT) and its automatic shifting accuracy play a significant role in driveline performance and reliability of vehicles. Consisting of an engine, a clutch, a gearbox, shafts, and wheels, an AMT system becomes complicated and complex process. Therefore, the fault detection and isolation (FDI) are not easily done and need to be improved. The main contribution of this study is a systematic design of a parity space-based FDI system directly from input-output data. Dynamic cases of faulty occurrence in clutch, engine speed, throttle angle, and three-shaft speed sensors are considered, and the proposed approach is shown to be successful in each case.

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.