关键词: 模型预测控制, 车辆横摆稳定性, 粒子群算法, 现场可编程门阵列, 实时实验

Abstract:

In order to improve the real-time performance of predictive controller for vehicle yaw stability control,we implement a predictive controller based on FPGA(Field Programmable Gate Array), and adopt PSO(Particle Swarm Optimization) combined with penalty function to solve the QP(Quadratic Programming) problem. The FPGA is used to reduce operation time of the MPC (Model Predictive Control) controller by using parallel computations. In order to verify the effectiveness of the controller, real-time tests are carried out in the typical vehicle running condition. In the tests, vehicle dynamic model in veDYNA is used as the plant, and the FPGA is used as the hardware platform of controller. The simulation results indicate that the MPC controller can satisfy the requirements of the vehicle yaw stability control well, and lay the foundation for the controller to real vehicle test.

Key words: model predictive control, vehicle yaw stability, particle swarm optimization, field programmable, gate array, real-time tests