Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal ...Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer.To address this issue,this study proposes a safe motion planning and control(SMPAC)framework for AVs.For the control layer,a dynamic model including multi-dimensional uncertainties is established.A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set.A flexible tube with varying cross-sections is constructed to reduce the controller conservatism.For the planning layer,a concept of safety sets,representing the geometric boundaries of the ego vehicle and obstacles under uncertainties,is proposed.The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories.An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles.A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC.The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.展开更多
Predictive control is an advanced control algorithm,which is widely used in industrial process control.Among them,model predictive control(MPC)is an important branch of predictive control.Its basic principle is to use...Predictive control is an advanced control algorithm,which is widely used in industrial process control.Among them,model predictive control(MPC)is an important branch of predictive control.Its basic principle is to use the system model to predict future behavior and determine the current control action by optimizing the objective function.This paper discusses the application of MPC in the prediction and control of the speed of vehicles to optimize traffic flow.It is a valuable reference for alleviating traffic congestion and improving travel efficiency and smoothness and provides scientific basis and technical support for future highway traffic management.展开更多
针对三相LCL并网逆三电平中点钳位(Neutral Point Clamped,NPC)逆变器中有限集模型预测控制(Finite Control Set-Model Predictive Control,FCS-MPC)计算量大和控制速度慢导致NPC逆变器性能变差的问题,设计了一种快速模型预测控制方法...针对三相LCL并网逆三电平中点钳位(Neutral Point Clamped,NPC)逆变器中有限集模型预测控制(Finite Control Set-Model Predictive Control,FCS-MPC)计算量大和控制速度慢导致NPC逆变器性能变差的问题,设计了一种快速模型预测控制方法。通过改变快速模型预测控制中参考电流电角度控制逆变器输出电流的相位,实现入网电流与电网同步;通过在冗余短矢量中选择合适的短矢量,实现直流侧中点电压平衡,减少计算量;通过缩小计算扇区,使快速模型预测控制仅在当前最优输出电压矢量附近进行寻优计算,进一步减少计算量。在MATLAB/SIMULINK中搭建了三相LCL并网NPC逆变器仿真模型,仿真结果验证了上述控制方法的快速性和可行性,运算速度提高了18.31%,入网电流总谐波失真(Total Harmonic Distortion,THD)值减少了2.38%。展开更多
基金supported by the National Natural Science Foundation of China(51875061)China Scholarship Council(202206050107)。
文摘Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer.To address this issue,this study proposes a safe motion planning and control(SMPAC)framework for AVs.For the control layer,a dynamic model including multi-dimensional uncertainties is established.A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set.A flexible tube with varying cross-sections is constructed to reduce the controller conservatism.For the planning layer,a concept of safety sets,representing the geometric boundaries of the ego vehicle and obstacles under uncertainties,is proposed.The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories.An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles.A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC.The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.
文摘Predictive control is an advanced control algorithm,which is widely used in industrial process control.Among them,model predictive control(MPC)is an important branch of predictive control.Its basic principle is to use the system model to predict future behavior and determine the current control action by optimizing the objective function.This paper discusses the application of MPC in the prediction and control of the speed of vehicles to optimize traffic flow.It is a valuable reference for alleviating traffic congestion and improving travel efficiency and smoothness and provides scientific basis and technical support for future highway traffic management.
文摘针对三相LCL并网逆三电平中点钳位(Neutral Point Clamped,NPC)逆变器中有限集模型预测控制(Finite Control Set-Model Predictive Control,FCS-MPC)计算量大和控制速度慢导致NPC逆变器性能变差的问题,设计了一种快速模型预测控制方法。通过改变快速模型预测控制中参考电流电角度控制逆变器输出电流的相位,实现入网电流与电网同步;通过在冗余短矢量中选择合适的短矢量,实现直流侧中点电压平衡,减少计算量;通过缩小计算扇区,使快速模型预测控制仅在当前最优输出电压矢量附近进行寻优计算,进一步减少计算量。在MATLAB/SIMULINK中搭建了三相LCL并网NPC逆变器仿真模型,仿真结果验证了上述控制方法的快速性和可行性,运算速度提高了18.31%,入网电流总谐波失真(Total Harmonic Distortion,THD)值减少了2.38%。
