The accurate control for the vehicle height and leveling adjustment system of an electronic air suspension (EAS) still is a challenging problem that has not been effectively solved in prior researches. This paper proposes a new adaptive controller to control the vehicle height and to adjust the roll and pitch angles of the vehicle body (leveling control) during the vehicle height adjustment procedures by an EAS system. A nonlinear mechanism model of the full-car vehicle height adjustment system is established to reflect the system dynamic behaviors and to derive the system optimal control law. To deal with the nonlinear characters in the vehicle height and leveling adjustment processes, the nonlinear system model is globally linearized through the state feedback method. On this basis, a fuzzy sliding mode controller (FSMC) is designed to improve the control accuracy of the vehicle height adjustment and to reduce the peak values of the roll and pitch angles of the vehicle body. To verify the effectiveness of the proposed control method more accurately, the full-car EAS system model programmed using AMESim is also given. Then, the co-simulation study of the FSMC performance can be conducted. Finally, actual vehicle tests are performed with a city bus, and the test results illustrate that the vehicle height adjustment performance is effectively guaranteed by the FSMC, and the peak values of the roll and pitch angles of the vehicle body during the vehicle height adjustment procedures are also reduced significantly. This research proposes an effective control methodology for the vehicle height and leveling adjustment system of an EAS, which provides a favorable control performance for the system.

In this article, an active collision avoidance based on improved artificial potential field is proposed to satisfy collision avoidance for intelligent vehicle. A longitudinal safety distance model based on analysis of braking process and a lane-changing safety spacing model based on minimum time of lane changing under the constraint of sideslip angle are presented. In addition, an improved artificial potential field method is introduced, which represents the influence of environmental information with artificial force. Simulation results demonstrate the superior performance of the proposed algorithm over collision avoidance for intelligent vehicle.

In this paper, a fault-tolerant control method for switched reluctance motors is proposed for the faults of power converters that occur during the operation of switched reluctance motors. The method combines position reconstruction strategies based on fault tolerant control. The fault detection scheme can accurately determine the faults component and faults type, and then and then maintain the motor drive through the current reconstruction. The position reconstruction scheme improves the traditional inductance detection method, which can improve the situation that the position signal fault leads to the current reconstruction fault. In addition, this paper sets constraints on the reconstruction of position signals to enhance the stability and robustness of position signals. Finally, the stability of the proposed method is verified by experiments.

Four-wheel driving/four-wheel steering vehicle has the advantages of high-speed stability, steering response, and safety, making them perform excellently under various driving conditions. However, it also faces challenges due to complex dynamic couplings, multiple control inputs, and computational burden. To address these problems, this paper proposes a multi-agent cooperative control architecture based on Cooperative Distributed Model Predictive Control (Co-DMPC). The paper explains the coupling mechanisms between agents by establishing distributed state equations and reduces the control inputs of each controller. It defines predicted trajectories, assumed trajectories, and optimal trajectories for states and control inputs, laying the foundation for the interaction of coupled information between agents. The impact of different agents on global performance indicators is measured by solving the cost coupling optimization problem. Furthermore, the iterative solution approach and the termination conditions address the balance between solution accuracy and computational efficiency. The Lyapunov function direct method is used to prove the asymptotic stability of both local and global systems. This paper quantitatively analyzes the influence of weight coefficients on control effects and presents an adaptive weight selection method based on road adhesion coefficient. Compared with centralized control, the proposed method has excellent tracking accuracy, response speed, and stability. Furthermore, the hardware-in-loop (HIL) test also demonstrate the performance of the proposed controller.