检索结果-南通市图书馆

Approximate Optimal Filter Design for Vehicle System through Actor‑Critic Reinforcement Learning

Automotive Innovation 2022年第4期5卷 415-426页

作者： Yuming Yin shengbo eben li Kaiming Tang Wenhan Cao Wei Wu Hongbo li School of Mechanical Engineering Zhejiang University of TechnologyHangzhou 310023China School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Beijing Geekplus Tech.Co. LtdBeijingChina

Precise state and parameter estimations are essential for identification,analysis and control of vehicle engineering problems,especially under significant model and measurement uncertainties.The widely used filtering/estimation algorithms,such as Kalman series like Kalman filter,extended Kalman filter,unscented Kalman filter,and particle filter,generally aim to approach the true state/parameter distribution via iteratively updating the filter gain at each time step.However,the optimal-ity of these filters would be deteriorated by unrealistic initial condition or significant model error.Alternatively,this paper proposes to approximate the optimal filter gain by considering the effect factors within infinite time horizon,on the basis of estimation-control duality.The proposed approximate optimal filter(AOF)problem is designed and subsequently solved by actor-critic reinforcement learning(RL)method.The AOF design transforms the traditional optimal filtering problem with the minimum expected mean square error into an optimal control problem with the minimum accumulated estimation error,in which the estimation error is used as the surrogate system state and the infinite-horizon filter gain is the control input.The estimation-control duality is proved to hold when certain conditions about initial vehicle state distributions and policy structure are maintained.In order to evaluate of the effectiveness of AOF,a vehicle state estimation problem is then demonstrated and compared with the steady-state Kalman filter.The results showed that the obtained filter policy via RL with different discount factors can converge to theoretical optimal gain with an error within 5%,and the average estimation errors of vehicle slip angle and yaw rate are less than 1.5×10–4.

关键词： Vehicle state estimation Kalman filter Estimation-control duality Reinforcement learning

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

FPGA accelerated model predictive control for autonomous driving

Journal of Intelligent and Connected Vehicles

引用

Journal of Intelligent and Connected Vehicles 2022年第2期5卷 63-71页

作者： Yunfei li shengbo eben li Xingheng Jia Shulin Zeng Yu Wang Tsinghua University BeijingChina and Chongqing UniversityChongqingChina Department of Automotive Engineering Tsinghua UniversityBeijingChina Tsinghua University BeijingChina

Purpose–The purpose of this paper is to reduce the difficulty of model predictive control(MPC)deployment on FPGA so that researchers can make better use of FPGA technology for academic research.Design/methodology/approach–In this paper,the MPC algorithm is written into FPGA by combining hardware with software.Experiments have verified this method.Findings–This paper implements a ZYNQ-based design method,which could significantly reduce the difficulty of development.The comparison with the CPU solution results proves that FPGA has a significant acceleration effect on the solution of MPC through the method.Research limitations implications–Due to the limitation of practical conditions,this paper cannot carry out a hardware-in-the-loop experiment for the time being,instead of an open-loop experiment.Originality value–This paper proposes a new design method to deploy the MPC algorithm to the FPGA,reducing the development difficulty of the algorithm implementation on FPGA.It greatly facilitates researchers in the field of autonomous driving to carry out FPGA algorithm hardware acceleration research.

关键词： FPGA Model predictive control Autonomous driving ZYNQ

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

引用

Control Theory and Technology 2022年第2期20卷 145-160页

作者： Fuguo Xu Hiroki Tsunogawa Junichi Kako Xiaosong Hu shengbo eben li Tielong Shen Lars Eriksson Carlos Guardiola Department of Engineering and Applied Sciences Sophia UniversityTokyo 102-8554Japan Higashi-Fuji Research Center Toyota Motor Corporation.Shizuoka 410-1107Japan The State Key Laboratory of Mechanical Transmissions Chongqing UniversityChongqing 400044China The State Key Laboratory of Automotive Safety and Energy Tsinghua UniversityBeijing 100084China Vehicular Systems Department Electrical EngineeringLinkoping UniversitySE 58183Linkoping.Sweden Departamento de Maquinas y Motores Termicos Universitat Politecnica de Valencia.Camino de Vera s/n46022 ValenciaSpain

In this paper,we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles(HEVs)on a road with slope.Moreover,it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything(V2X),including geographic information,vehicle-to-infrastructure(V2I)information and vehicle-to-vehicle(V2V)information.The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator,where the running route is generated based on real-world data with slope and intersection position.The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time.To show the HEV powertrain characteristics,a case study is given with the speed planning and energy management strategy.

关键词： Powertrain control Connected and automated vehicles Hybrid electric vehicles Vehicle-to-everything

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

GOPS:A general optimal control problem solver for autonomous driving and industrial control applications

引用

Communications in Transportation Research 2023年第1期3卷 92-106页

作者： Wenxuan Wang Yuhang Zhang Jiaxin Gao Yuxuan Jiang Yujie Yang Zhilong Zheng Wenjun Zou Jie li Congsheng Zhang Wenhan Cao Genjin Xie Jingliang Duan shengbo eben li School of Vehicle and Mobility Tsinghua UniversityBeijing100084China

Solving optimal control problems serves as the basic demand of industrial control tasks.Existing methods like model predictive control often suffer from heavy online computational burdens.Reinforcement learning has shown promise in computer and board games but has yet to be widely adopted in industrial applications due to a lack of accessible,high-accuracy solvers.Current Reinforcement learning(RL)solvers are often developed for academic research and require a significant amount of theoretical knowledge and programming skills.Besides,many of them only support Python-based environments and limit to model-free algorithms.To address this gap,this paper develops General Optimal control Problems Solver(GOPS),an easy-to-use RL solver package that aims to build real-time and high-performance controllers in industrial fields.GOPS is built with a highly modular structure that retains a flexible framework for secondary development.Considering the diversity of industrial control tasks,GOPS also includes a conversion tool that allows for the use of Matlab/Simulink to support environment construction,controller design,and performance validation.To handle large-scale problems,GOPS can automatically create various serial and parallel trainers by flexibly combining embedded buffers and samplers.It offers a variety of common approximate functions for policy and value functions,including polynomial,multilayer perceptron,convolutional neural network,etc.Additionally,constrained and robust algorithms for special industrial control systems with state constraints and model uncertainties are also integrated into GOPS.Several examples,including linear quadratic control,inverted double pendulum,vehicle tracking,humanoid robot,obstacle avoidance,and active suspension control,are tested to verify the performances of GOPS.

关键词： Industrial control Reinforcement learning Approximate dynamic programming Optimal control Neural network Benchmark

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Robust cooperation of connected vehicle systems with eigenvalue-bounded interaction topologies in the presence of uncertain dynamics

引用

Frontiers of Mechanical Engineering 2018年第3期13卷 354-367页

作者： Keqiang li Feng GAO shengbo eben li Yang ZHENG Hongbo GAO State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing 100084 China School of Automotive Engineering Chongqing University Chongqing 400044 China Department of Engineering Science University of Oxford Oxford OX13PJ UK

This study presents a distributed H-infinity control method for uncertain platoons with dimensionally and structurally unknown interaction topologies provided that the associated topological eigenvalues are bounded by a predesigned range. With an inverse model to compensate for nonlinear powertrain dynamics, vehicles in a platoon are modeled by third-order uncertain systems with bounded disturbances. On the basis of the eigenvalue decomposition of topological matrices, we convert the platoon system to a norm-bounded uncertain part and a diagonally structured certain part by applying linear transformation. We then use a common Lyapunov method to design a distributed H-infinity controller. Numerically, two linear matrix inequalities corresponding to the minimum and maximum eigenvalues should be solved. The resulting controller can tolerate interaction topologies with eigenvalues located in a certain range. The proposed method can also ensure robustness performance and disturbance attenuation ability for the closed-loop platoon system. Hardware-in-the-loop tests are performed to validate the effectiveness of our method.

关键词： automated vehicles platoon distributedcontrol robustness

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Stability of General linear Dynamic Multi-Agent Systems under Switching Topologies with Positive Real Eigenvlues

引用

Engineering 2020年第6期6卷 688-694页

作者： shengbo eben li Zhitao Wang Yang Zheng Diange Yang Keyou You State Key Lab of Automotive Safety and Energy School of Vehicle and MobilityTsinghua UniversityBeijing 100084China Department of Engineering Science Balliol CollegeUniversity of OxfordOxford 0X13PJUK Department of Automation Tsinghua UniversityBeijing 100084China

The time-varying network topology can significantly affect the stability of multi-agent systems.This paper examines the stability of leader-follower multi-agent systems with general linear dynamics and switching network topologies,which have applications in the platooning of connected vehicles.The switching interaction topology is modeled as a class of directed graphs in order to describe the information exchange between multi-agent systems,where the eigenvalues of every associated matrix are required to be positive real.The Hurwitz criterion and the Riccati inequality are used to design a distributed control law and estimate the convergence speed of the closed-loop system.A sufficient condition is provided for the stability of multi-agent systems under switching topologies.A common Lyapunov function is formulated to prove closed-loop stability for the directed network with switching topologies.The result is applied to a typical cyber-physical system—that is,a connected vehicle platoon—which illustrates the effectiveness of the proposed method.

关键词： Stability Multi-agent system Switching topologies Common Lyapunov function

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Self-learning drift control of automated vehicles beyond handling limit after rear-end collision

引用

Transportation Safety and Environment 2020年第2期2卷 97-105页

作者： Yuming Yin shengbo eben li Keqiang li Jue Yang Fei Ma School of Vehicle and Mobility Tsinghua UniversityBeijing100084China Department of Vehicle Engineering Zhejiang University of TechnologyHangzhou310023China Department of Vehicle Engineering University of Science and Technology BeijingBeijing100083China

Vehicles involved in traffic accidents generally experience divergent vehicle motion,which causes severe damage.This paper presents a self-learning drift-control method for the purpose of stabilizing a vehicle’s yaw motions after a high-speed rear-end collision.The struck vehicle generally experiences substantial drifting and/or spinning after the collision,which is beyond the handling limit and difficult to control.Drift control of the struck vehicle along the original lane was investigated.The rear-end collision was treated as a set of impact forces,and the three-dimensional non-linear dynamic responses of the vehicle were considered in the drift control.A multi-layer perception neural network was trained as a deterministic control policy using the actor-critic reinforcement learning framework.The control policy was iteratively updated,initiating from a random parameterized policy.The results show that the self-learning controller gained the ability to eliminate unstable vehicle motion after data-driven training of about 60,000 iterations.The controlled struck vehicle was also able to drift back to its original lane in a variety of rear-end collision scenarios,which could significantly reduce the risk of a second collision in traffic.

关键词： automated vehicle drift control reinforcement learning rear-end collision

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

End-to-End Autonomous Driving Through Dueling Double Deep Q-Network

引用

Automotive Innovation 2021年第3期4卷 328-337页

作者： Baiyu Peng Qi Sun shengbo eben li Dongsuk Kum Yuming Yin Junqing Wei Tianyu Gu State Key Lab of Automotive Safety and Energy School of Vehicle and MobilityTsinghua UniversityBeijing 100084China Korea Advanced Institute of Science and Technology 193Munji-roYuseong-guDaejeonKorea DiDi Autonomous Driving Company Beijing 100084China

Recent years have seen the rapid development of autonomous driving systems,which are typically designed in a hierarchical architecture or an end-to-end architecture.The hierarchical architecture is always complicated and hard to design,while the end-to-end architecture is more promising due to its simple structure.This paper puts forward an end-to-end autonomous driving method through a deep reinforcement learning algorithm Dueling Double Deep Q-Network,making it possible for the vehicle to learn end-to-end driving by itself.This paper firstly proposes an architecture for the end-to-end lane-keeping task.Unlike the traditional image-only state space,the presented state space is composed of both camera images and vehicle motion information.Then corresponding dueling neural network structure is introduced,which reduces the variance and improves sampling efficiency.Thirdly,the proposed method is applied to The Open Racing Car Simulator(TORCS)to demonstrate its great performance,where it surpasses human drivers.Finally,the saliency map of the neural network is visualized,which indicates the trained network drives by observing the lane lines.A video for the presented work is available online,https://youtu.be/76ciJ mIHMD8 or https://***/v_show/id_XNDM4 ODc0M TM4NA==.html.

关键词： End-to-end autonomous driving Reinforcement learning Deep Q-network Neural network

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Markov probabilistic decision making of self-driving cars in highway with random traffic flow: a simulation study

Journal of Intelligent and Connected Vehicles

引用

Journal of Intelligent and Connected Vehicles 2018年第2期1卷 77-84页

作者： Yang Guan shengbo eben li Jingliang Duan Wenjun Wang Bo Cheng Tsinghua University BeijingChina Department of Automotive Engineering Tsinghua UniversityBeijingChina

Purpose–Decision-making is one of the key technologies for self-driving cars.The high dependency of previously existing methods on human driving data or rules makes it difﬁcult to model policies for different driving situations.Design/methodology/approach–In this research,a probabilistic decision-making method based on the Markov decision process(MDP)is proposed to deduce the optimal maneuver automatically in a two-lane highway scenario without using any human data.The decision-making issues in a trafﬁc environment are formulated as the MDP by deﬁning basic elements including states,actions and basic models.Transition and reward models are deﬁned by using a complete prediction model of the surrounding cars.An optimal policy was deduced using a dynamic programing method and evaluated under a two-dimensional simulation environment.Findings–Results show that,at the given scenario,the self-driving car maintained safety and efﬁciency with the proposed policy.Originality/value–This paper presents a framework used to derive a driving policy for self-driving cars without relying on any human driving data or rules modeled by hand.

关键词： Markov decision process Decision-making Dynamic programming Self-driving cars

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

依托多风格强化学习的车辆轨迹跟踪避撞控制

引用

汽车工程 2024年第6期46卷 945-955页

作者：肖礼明张发旺陈良发闫昊琪马飞李升波段京良北京科技大学机械工程学院北京100083 北京理工大学机械与车辆学院北京100081 清华大学车辆与运载学院北京100084

轨迹跟踪避撞是车辆智能性的重要体现,针对现有控制方法面对同一场景的控制风格单一问题,本文中提出了一种多风格型强化学习控制方法。为实现控制风格多样性,首次将风格指标引入值网络和策略网络,搭建了多风格跟踪避撞策略网络,并结合... 详细信息

轨迹跟踪避撞是车辆智能性的重要体现,针对现有控制方法面对同一场景的控制风格单一问题,本文中提出了一种多风格型强化学习控制方法。为实现控制风格多样性,首次将风格指标引入值网络和策略网络,搭建了多风格跟踪避撞策略网络,并结合值分布强化学习理论构建了多风格策略迭代框架,依托该框架推导提出了多风格值分布强化学习算法。仿真和实车试验表明:所提出方法可以多种驾驶风格(激进、中性、保守)完成轨迹跟踪避撞任务,实车稳态轨迹跟踪误差小于5cm,具备较高的控制精度,实车平均单步决策耗时仅为6.07ms,满足实时性要求。

关键词：多风格值分布强化学习轨迹跟踪主动避撞

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：