模型设计(Model based Design,MBD)方法被广泛用于汽车嵌入式设计开发。近些年随着智能网联汽车的发展,其Simulink模型也越来越复杂,从上万行代码跃升至百万级以上代码。因此需要对功能模型进行适当的分解,以促进代码的可维护性、可理...模型设计(Model based Design,MBD)方法被广泛用于汽车嵌入式设计开发。近些年随着智能网联汽车的发展,其Simulink模型也越来越复杂,从上万行代码跃升至百万级以上代码。因此需要对功能模型进行适当的分解,以促进代码的可维护性、可理解性。设计并搭建了基于MBD的线控底盘微缩模型。以Simulink模块化编程为基础、结合控制理论、车辆运动学以及电气工程相关知识,完成了无人驾驶阿克曼底盘的仿真模型,并将仿真代码烧录到实车。该实验系统能够用于实验教学,并通过基于MBD教学方法,提升学生线控底盘的设计能力。展开更多
Today,many hybrid multilevel converters with flying capacitors are being proposed.Besides the practicality of these converters in reducing the switching devices count and the cost,it is challenging to balance their ca...Today,many hybrid multilevel converters with flying capacitors are being proposed.Besides the practicality of these converters in reducing the switching devices count and the cost,it is challenging to balance their capacitor voltage charge.The unbalanced capacitors restrain the converters’performance and lead to failure in delivering the required current.This paper proposes a model predictive control(MPC)for a seven-level converter based on an active neutral point clamped converter cascaded with an H-bridge.A conventional MPC uses a single cost function with two terms,one for current tracking and the other for capacitor balancing,which needs an accurate weighting factor to balance them.In this work,the suggested MPC adopts a satisfactory optimization technique.It evaluates the capacitor charge membership to a nominal voltage range to define the degree of freedom in which to optimize the current tracking problem.This transforms the relationship between the two cost terms into a more collaborative relationship.The proposed MPC improves the output current quality and balances the capacitor charge with the least number of computations.Experimental and simulation results have validated the controller^effectiveness.展开更多
The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making(IDM)systems.Consequently,IDM shoul...The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making(IDM)systems.Consequently,IDM should possess the ability to continuously acquire new skills and effectively generalize across a broad range of applications.The advancement of Artificial General Intelligence(AGI)that transcends task and application boundaries is critical for enhancing IDM.Recent studies have extensively investigated the Transformer neural architecture as a foundational model for various tasks,including computer vision,natural language processing,and reinforcement learning.We propose that a Foundation Decision Model(FDM)can be developed by formulating diverse decision-making tasks as sequence decoding tasks using the Transformer architecture,offering a promising solution for expanding IDM applications in complex real-world situations.In this paper,we discuss the efficiency and generalization improvements offered by a foundation decision model for IDM and explore its potential applications in multi-agent game AI,production scheduling,and robotics tasks.Lastly,we present a case study demonstrating our FDM implementation,DigitalBrain(DB1)with 1.3 billion parameters,achieving human-level performance in 870 tasks,such as text generation,image captioning,video game playing,robotic control,and traveling salesman problems.As a foundation decision model,DB1 represents an initial step toward more autonomous and efficient real-world IDM applications.展开更多
文摘模型设计(Model based Design,MBD)方法被广泛用于汽车嵌入式设计开发。近些年随着智能网联汽车的发展,其Simulink模型也越来越复杂,从上万行代码跃升至百万级以上代码。因此需要对功能模型进行适当的分解,以促进代码的可维护性、可理解性。设计并搭建了基于MBD的线控底盘微缩模型。以Simulink模块化编程为基础、结合控制理论、车辆运动学以及电气工程相关知识,完成了无人驾驶阿克曼底盘的仿真模型,并将仿真代码烧录到实车。该实验系统能够用于实验教学,并通过基于MBD教学方法,提升学生线控底盘的设计能力。
基金supported in part by the National Natural Science Foundation of China under Grant No.61873166,61673275 and 61473184.
文摘Today,many hybrid multilevel converters with flying capacitors are being proposed.Besides the practicality of these converters in reducing the switching devices count and the cost,it is challenging to balance their capacitor voltage charge.The unbalanced capacitors restrain the converters’performance and lead to failure in delivering the required current.This paper proposes a model predictive control(MPC)for a seven-level converter based on an active neutral point clamped converter cascaded with an H-bridge.A conventional MPC uses a single cost function with two terms,one for current tracking and the other for capacitor balancing,which needs an accurate weighting factor to balance them.In this work,the suggested MPC adopts a satisfactory optimization technique.It evaluates the capacitor charge membership to a nominal voltage range to define the degree of freedom in which to optimize the current tracking problem.This transforms the relationship between the two cost terms into a more collaborative relationship.The proposed MPC improves the output current quality and balances the capacitor charge with the least number of computations.Experimental and simulation results have validated the controller^effectiveness.
文摘The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making(IDM)systems.Consequently,IDM should possess the ability to continuously acquire new skills and effectively generalize across a broad range of applications.The advancement of Artificial General Intelligence(AGI)that transcends task and application boundaries is critical for enhancing IDM.Recent studies have extensively investigated the Transformer neural architecture as a foundational model for various tasks,including computer vision,natural language processing,and reinforcement learning.We propose that a Foundation Decision Model(FDM)can be developed by formulating diverse decision-making tasks as sequence decoding tasks using the Transformer architecture,offering a promising solution for expanding IDM applications in complex real-world situations.In this paper,we discuss the efficiency and generalization improvements offered by a foundation decision model for IDM and explore its potential applications in multi-agent game AI,production scheduling,and robotics tasks.Lastly,we present a case study demonstrating our FDM implementation,DigitalBrain(DB1)with 1.3 billion parameters,achieving human-level performance in 870 tasks,such as text generation,image captioning,video game playing,robotic control,and traveling salesman problems.As a foundation decision model,DB1 represents an initial step toward more autonomous and efficient real-world IDM applications.
