Yaw compensation and yaw relaxation controls for active steering of railway wheelsets via electromechanical actuators

It is known that good curving performance and stability often have conflicting requirements given a passive yaw stiffness of the wheelset. Using an active steering system, however, has the potential to realize improved curving performance with a satisfactory running stability. Relatively simple active control solutions of yaw relaxation and yaw compensation are illustrated and compared in this paper. In both control solutions, only low-cost electromechanical actuators and load cells are adopted for low-frequency actuations. Associated with a prototype of the two-axle vehicle, the dynamic performances of yaw relaxation and yaw compensation controls for different yaw stiffness configurations are simulated. The homogenous simulation results demonstrate excellent dynamic performance in curve negotiation and stability with the active steering strategies adopted.

A model free controller based on reinforcement learning for active steering system with uncertainties

Vehicle steering control is crucial to autonomous vehicles. However, unknown parameters and uncertainties of vehicle steering systems bring a great challenge to its control performance, which needs to be tackled urgently. Therefore, this paper proposes a novel model free controller based on reinforcement learning for active steering system with unknown parameters. The model of the active steering system and the Brushless Direct Current (BLDC) motor is built to construct a virtual object in simulations. The agent based on Deep Deterministic Policy Gradient (DDPG) algorithm is built, including actor network and critic network. The rewards from environment are designed to improve the effectiveness of agent. Simulations and testbench experiments are implemented to train the agent and verify the effectiveness of the controller. Results show that the proposed algorithm can acquire the network parameters and achieve effective control performance without any prior knowledges or models. The proposed agent can adapt to different vehicles or active steering systems easily and effectively with only retraining of the network parameters.

Fuzzy and Sliding Mode Variable Structure Control of Vehicle Active Steering System

Background: All the time, the safety of vehicle has been valued by all the world's parties, whether it is now or in the future, the automobile safety issue is the hotspot and focus of the research by experts and scholars both at home and abroad. The continuous increase of car ownership brings convenience to people's life and it also poses a threat to people's life and property security. Objective: Vehicle active safety system is the. hotspot of current research and development, which plays an important role in automobile safety. Through the analysis of patents and references, understand the development of an active steering system.In order to improve the development efficiency of active steering system, the paper proposes a feedback control method of front wheel angle. Methods: Based on yaw velocity and center of mass side angle, the Active Front Steering (AFS)model is established respectively by fuzzy control and sliding mode control under the establishment of seven degrees of freedom vehicle dynamics model and Dug off tire model. Results: The simulation results show that both the control algorithm of sliding mode control and fuzzy control can improve the handling stability of vehicle steering on high adhesion coefficient road surface. On the low adhesion coefficient road, the control effect of slide mode control is more ideal while fuzzy control caused larger oversteer. Conclusion: The simulation results show that the control effect of sliding mode is superior to fuzzy control.

Research on Model Predictive Control Method for Vehicle Lateral Stability Based on Hardware-in-the-Loop Test

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.