Active steering technology is a key technology for automatic driving vehicles to achieve route tracking and obstacle avoidance and risk avoidance, and its performance will affect the stability control of the vehicle. For solving the stability control issues of vehicles, which have uncertainty in model and robustness in system, this paper proposes an active steering control method based on the receding horizon control model. It calculates the optimal control law by this method by using the real-time vehicle state so that it can compensate for the uncertainty caused by model mismatch, interference, etc. The design of the controller is implemented by using the yaw rate deviation of the vehicle as the input of the receding horizon linear quadratic controller model and then inputting the calculated superposition angle into the vehicle model in real time. We built a Simulink control model to implement co-simulation with CarSim to verify the control effect of the controller. In addition, we built a steering hardware-in-the-loop platform based on the LabVIEW RT system. The experimental results show that the active steering system adopting a receding horizon control method had better system robustness and robust stability.

High-pressure vacuum contactor is an important primary equipment for high-voltage power plant system in power plants. It has the advantages of low noise, long service life and frequent operation. It is mainly used for the control of high-voltage motors in factories. If the switch is repeatedly switched on and off, it will cause great impact force on the stator coil and rotor squirrel cage bar of the motor, and damage the insulation of the winding and cause overheating and fracture of the squirrel cage bar. At the same time, it may also cause bus grounding, damage or even explosion of the switch contacts. Therefore, it is necessary to analyze and discuss the fault causes of the anti-jump control loop of the high-voltage vacuum contactor of the power plant, and seek to solve the "anti-jump" scheme. The control loop is optimized by analysis to solve the fault, and technical support is also provided for the anti-jump circuit of the 6kV vacuum switch and the FC switch.

With the rapid development of the vehicle chassis control and autonomous driving technology, it is more and more urgent to realize the active steering technology of autonomous driving stability control. Under emergency conditions, the adhesion constraints, the model uncertainty, and the strong nonlinearity of vehicle bring great challenges to active steering control. In this paper, a model predictive control method for an active steering system based on a nonlinear vehicle model is proposed to solve the problems of adhesion constraint, model uncertainty, and external disturbance in the active steering system. Based on the real-time measurement of vehicle state, a new optimization method is proposed in this paper, which has good performance in dealing with the uncertainty and nonlinearity of the model. The control method transforms the constraint problem into quadratic programming and nonlinear programming. In order to ensure the control accuracy when the vehicle enters the nonlinear area, the control model is built with the combination of the nonlinear tire model and the 2DOF model. The control model is built based on Simulink, and the effectiveness of the controller is the verified joint simulation of Simulink and CarSim. The hardware-in-the-loop (HIL) test bench based on LabVIEW RT is built and tested in order to verify the feasibility and real effect of the controller. Simulation and HIL test results demonstrate that, compared with PID controller, the model predictive controller can accomplish the driving task well and improve the vehicle handling stability.