This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed ra...This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed rapidly,moving from basic driver-assistance systems(Level 1)to fully autonomous capabilities(Level 5).Central to this advancement are two key functionalities:Lane-Change Maneuvers(LCM)and Adaptive Cruise Control(ACC).In this study,a detailed simulation environment is created to replicate the road network between Nantun andWuri on National Freeway No.1 in Taiwan.The MPC controller is deployed to optimize vehicle trajectories,ensuring safe and efficient navigation.Simulated onboard sensors,including vehicle cameras and millimeterwave radar,are used to detect and respond to dynamic changes in the surrounding environment,enabling real-time decision-making for LCM and ACC.The simulation resultshighlight the superiority of the MPC-based approach in maintaining safe distances,executing controlled lane changes,and optimizing fuel efficiency.Specifically,the MPC controller effectively manages collision avoidance,reduces travel time,and contributes to smoother traffic flow compared to traditional path planning methods.These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios.Future research will focus on validating these results through real-world testing,addressing computational challenges for real-time implementation,and exploring the adaptability of MPC under various environmental conditions.This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation,paving the way for broader adoption of MPC in AV systems.展开更多
The current research of autonomous vehicle motion control mainly focuses on trajectory tracking and velocity tracking. However, numerous studies deal with trajectory tracking and velocity tracking separately, and the ...The current research of autonomous vehicle motion control mainly focuses on trajectory tracking and velocity tracking. However, numerous studies deal with trajectory tracking and velocity tracking separately, and the yaw stability is seldom considered during trajectory tracking. In this research, a combination of the longitudinal–lateral control method with the yaw stability in the trajectory tracking for autonomous vehicles is studied. Based on the vehicle dynamics, considering the longitudinal and lateral motion of the vehicle, the velocity tracking and trajectory tracking problems can be attributed to the longitudinal and lateral control. A sliding mode variable structure control method is used in the longitudinal control. The total driving force is obtained from the velocity error in order to carry out velocity tracking. A linear time-varying model predictive control method is used in the lateral control to predict the required front wheel angle for trajectory tracking. Furthermore, a combined control framework is established to control the longitudinal and lateral motions and improve the reliability of the longitudinal and lateral direction control. On this basis, the driving force of a tire is allocated reasonably by using the direct yaw moment control, which ensures good yaw stability of the vehicle when tracking the trajectory. Simulation results indicate that the proposed control strategy is good in tracking the reference velocity and trajectory and improves the performance of the stability of the vehicle.展开更多
Based on the detailed analysis of the third coke oven in BaoSteel, a feedbackcontrol strategy of longitudinal temperature and finished carbonization time of coke ovens wasproposed and it was applied to the third coke ...Based on the detailed analysis of the third coke oven in BaoSteel, a feedbackcontrol strategy of longitudinal temperature and finished carbonization time of coke ovens wasproposed and it was applied to the third coke oven in BaoSteel. As a result, the ratio of theinstance that the absolute deviation of the longitudinal temperature is within +- 7 deg C and thefinished carbonization time within +- 10 min is more than 80 percent, having acquired the patentsaving effect of an energy consumption lowered by 2.92 percent. At the same time, it can provide anexample for the same coke ovens inside and outside the nation.展开更多
Railway train energy simulation is an important and popular research topic.Locomotive traction force simulations are a fundamental part of such research.Conventional energy calculation models are not able to consider ...Railway train energy simulation is an important and popular research topic.Locomotive traction force simulations are a fundamental part of such research.Conventional energy calculation models are not able to consider locomotive wheel-rail adhesions,traction adhesion control,and locomotive dynamics.This paper has developed two models to fill this research gap.The first model uses a 2D locomotive model with 27 degrees of freedom and a simplified wheel-rail contact model.The second model uses a 3D locomotive model with 54 degrees of freedom and a fully detailed wheel-rail contact model.Both models were integrated into a longitudinal train dynamics model with the consideration of locomotive adhesion control.Energy consumption simulations using a conventional model(1D model)and the two new models(2D and 3D models)were conducted and compared.The results show that,due to the consideration of wheel-rail adhesion model and traction control in the 3D model,it reports less energy consumption than the 1D model.The maximum difference in energy consumption rate between the 3D model and the 1D model was 12.5%.Due to the consideration of multiple wheel-rail contact points in the 3D model,it reports higher energy consumption than the 2D model.An 8.6%maximum difference in energy consumption rate between the 3D model and the 1D model was reported during curve negotiation.展开更多
基金National Science and Technology Council,Taiwan,for financially supporting this research(Grant No.NSTC 113-2221-E-018-011)Ministry of Education’s Teaching Practice Research Program,Taiwan(PSK1120797 and PSK1134099).
文摘This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed rapidly,moving from basic driver-assistance systems(Level 1)to fully autonomous capabilities(Level 5).Central to this advancement are two key functionalities:Lane-Change Maneuvers(LCM)and Adaptive Cruise Control(ACC).In this study,a detailed simulation environment is created to replicate the road network between Nantun andWuri on National Freeway No.1 in Taiwan.The MPC controller is deployed to optimize vehicle trajectories,ensuring safe and efficient navigation.Simulated onboard sensors,including vehicle cameras and millimeterwave radar,are used to detect and respond to dynamic changes in the surrounding environment,enabling real-time decision-making for LCM and ACC.The simulation resultshighlight the superiority of the MPC-based approach in maintaining safe distances,executing controlled lane changes,and optimizing fuel efficiency.Specifically,the MPC controller effectively manages collision avoidance,reduces travel time,and contributes to smoother traffic flow compared to traditional path planning methods.These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios.Future research will focus on validating these results through real-world testing,addressing computational challenges for real-time implementation,and exploring the adaptability of MPC under various environmental conditions.This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation,paving the way for broader adoption of MPC in AV systems.
基金Supported by National Natural Science Foundation of China(Grant Nos.51575103,11672127,U1664258)Fundamental Research Funds for the Central Universities of China(Grant No.NT2018002)+1 种基金China Postdoctoral Science Foundation(Grant Nos.2017T100365,2016M601799)the Fundation of Graduate Innovation Center in NUAA(Grant No.k j20180207)
文摘The current research of autonomous vehicle motion control mainly focuses on trajectory tracking and velocity tracking. However, numerous studies deal with trajectory tracking and velocity tracking separately, and the yaw stability is seldom considered during trajectory tracking. In this research, a combination of the longitudinal–lateral control method with the yaw stability in the trajectory tracking for autonomous vehicles is studied. Based on the vehicle dynamics, considering the longitudinal and lateral motion of the vehicle, the velocity tracking and trajectory tracking problems can be attributed to the longitudinal and lateral control. A sliding mode variable structure control method is used in the longitudinal control. The total driving force is obtained from the velocity error in order to carry out velocity tracking. A linear time-varying model predictive control method is used in the lateral control to predict the required front wheel angle for trajectory tracking. Furthermore, a combined control framework is established to control the longitudinal and lateral motions and improve the reliability of the longitudinal and lateral direction control. On this basis, the driving force of a tire is allocated reasonably by using the direct yaw moment control, which ensures good yaw stability of the vehicle when tracking the trajectory. Simulation results indicate that the proposed control strategy is good in tracking the reference velocity and trajectory and improves the performance of the stability of the vehicle.
文摘Based on the detailed analysis of the third coke oven in BaoSteel, a feedbackcontrol strategy of longitudinal temperature and finished carbonization time of coke ovens wasproposed and it was applied to the third coke oven in BaoSteel. As a result, the ratio of theinstance that the absolute deviation of the longitudinal temperature is within +- 7 deg C and thefinished carbonization time within +- 10 min is more than 80 percent, having acquired the patentsaving effect of an energy consumption lowered by 2.92 percent. At the same time, it can provide anexample for the same coke ovens inside and outside the nation.
基金The editing contribution of Mr.Tim McSweeney(Adjunct Research Fellow,Centre for Railway Engineering)is gratefully acknowledged.
文摘Railway train energy simulation is an important and popular research topic.Locomotive traction force simulations are a fundamental part of such research.Conventional energy calculation models are not able to consider locomotive wheel-rail adhesions,traction adhesion control,and locomotive dynamics.This paper has developed two models to fill this research gap.The first model uses a 2D locomotive model with 27 degrees of freedom and a simplified wheel-rail contact model.The second model uses a 3D locomotive model with 54 degrees of freedom and a fully detailed wheel-rail contact model.Both models were integrated into a longitudinal train dynamics model with the consideration of locomotive adhesion control.Energy consumption simulations using a conventional model(1D model)and the two new models(2D and 3D models)were conducted and compared.The results show that,due to the consideration of wheel-rail adhesion model and traction control in the 3D model,it reports less energy consumption than the 1D model.The maximum difference in energy consumption rate between the 3D model and the 1D model was 12.5%.Due to the consideration of multiple wheel-rail contact points in the 3D model,it reports higher energy consumption than the 2D model.An 8.6%maximum difference in energy consumption rate between the 3D model and the 1D model was reported during curve negotiation.
