检索结果-内蒙古大学图书馆

2024 International Conference on Intelligent Robotics and Automatic Control

作者： Cao, Jingshuai Wang, Tao Yang, Bingsan Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Peoples R China

ISBN: (纸本)9798350389814;9798350389807

In this paper, a high speed flight vehicle intelligent control method based on the proximal policy optimization (ppo) algorithm is researched. This method can be used to avoid the impact of interceptor employing the pure proportional navigation (PPN) guidance strategy in three dimensions. First, the interception process of the interceptor and the penetration process of the flight vehicle in the actual scenario were analyzed. The flight vehicle kinematic model, the interceptor kinematic model based on the PPN guidance law, and the relative motion model were constructed. Then, a flight vehicle intelligent penetration guidance law training model was built with specific scenarios. ppo-based model structure, state space, action space, reward function and training end conditions were designed. Finally, an intelligent flight vehicle penetration control model is formed through training, and the effectiveness of the proposed penetration method is verified through simulation tests. The method is applied to penetrate other performance interceptors and compared with other maneuvering strategies. The results verify the robustness and significant advantages of the intelligent penetration method. The intelligent penetration method studied in this paper has certain reference value for the flight vehicle guidance law design.

关键词： High speed flight vehicle Penetration Intelligent maneuver Deep reinforcement learning ppo algorithm PPN

来源：评论

学校读者我要写书评

暂无评论

Research on Manipulator Control Strategy based on ppo algorithm

Research on Manipulator Control Strategy based on PPO Algori...

引用

2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Liu, Shuai Sichuan Vocational and Technical College Suining629000 China

ISBN: (纸本)9798350308167

With the continuous development of science and technology, as a common industrial robot automation equipment, the research on its control strategy has always been a hot spot in related fields, but there is a problem of inaccurate result evaluation. Traditional control algorithms cannot solve the problem of adaptive training learning in complex high-dimensional environments, and the evaluation is unreasonable. Therefore, this paper proposes a ppo algorithm for innovative optimization control system analysis. First, the manipulator control theory is used to evaluate the manipulator and carry out it according to the requirements of the control system Indicator division to reduce interference factors in the control system. Then, the manipulator control related theory analyzes the manipulator control system, forms the control system scheme, and corrects Comprehensive analysis of control system results. The simulation environment reference shows that under the condition of certain evaluation criteria, the ppo algorithm has an accuracy on the control system of the robotic arm The real-time performance of the control algorithm is better than that of the traditional control algorithm. © 2023 IEEE.

关键词： automation equipment ppo algorithm robotic arm Robots

来源：评论

学校读者我要写书评

暂无评论

Improved ppo algorithm for USV Path Planning

Improved PPO Algorithm for USV Path Planning

引用

International Conference on Guidance, Navigation and Control, ICGNC 2024

作者： Zhang, Yunfeng Yang, Shujie Jin, Junjie Li, Shuaicheng Gao, Yuchen Zhejiang Ocean University Zhoushan316022 China

ISBN: (纸本)9789819622313

The Proximal Policy Optimization (ppo) algorithm is known to have issues such as extended training time and repetitive exploration, attributed to its high computational capacity and uneven sampling exploration. Therefore, this paper proposes the integration of the International Regulations for Preventing Collisions at Sea (COLREGs) as constraints within the ppo algorithm. Specifically, the global reference path obtained from the Artificial Potential Field (APF) method is utilized to construct the range field function, which in turn shapes the reward function and guides exploration direction. Additionally, APF's path nodes are inputted into the internal experience buffer and policy network of the ppo algorithm, allowing for emulation of ideal behavior patterns by USV during initial learning phases and avoidance of invalid path attempts. Finally, output paths are smoothed to enhance navigation efficiency. Simulation experiments demonstrate that the enhanced ppo algorithm accelerates exploration time per step by 47.72% compared to the unimproved version, with approximately 53.33% faster convergence steps - resulting in improved economy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial Potential Field Method Path Smoothing Process ppo algorithm Route Planning

来源：评论

学校读者我要写书评

暂无评论

A multi-user mobile edge computing task offloading and trajectory management based on proximal policy optimization

引用

PEER-TO-PEER NETWORKING AND APPLICATIONS 2024年第6期17卷 4210-4229页

作者： Ma, Bo Xu, Yong Pan, Yexin Liu, Shiyuan Li, Chuanhuang Zhejiang Gongshang Univ Sch Informat & Elect Engn Xuezheng St Hangzhou 310018 Zhejiang Peoples R China

Mobile Edge Computing (MEC) has gained significant attention in recent years due to its capability of providing computation and storage resources closer to end-users. However, existing research has focused on addressing MEC resource optimization problems with explicit location information and has not considered the challenges posed by users who refuse to grant system access to Global Positioning System (GPS) information. To address this issue, this paper models the location-protected MEC problem as an Integer Non-Linear Problem (INLP) and proposes a novel mechanism to cater to various types of users while optimizing both computation time and service latency. This problem is proposed to be solved by decoupling it into a path recommendation problem and a task offloading problem. For different user types, a Proximal Policy Optimization (ppo) algorithm is proposed to recommend user paths. By following these paths, multi-user interference is minimized. Then, the Deep Q-Network (DQN) algorithm is employed to determine MEC offloading strategies to solve the task offloading problem. Experimental results demonstrate that the algorithm outperforms related approaches across multiple dimensions, including computation time, service latency, and stability. Additionally, the proposed mechanism effectively addresses the offloading challenge for privacy-conscious users in edge computing scenarios. The proposed mechanism has several potential applications, such as resource optimization in mobile cloud computing, the Internet of Things (IoT), and smart cities. It also addresses optimization problems for various types of users in mobile edge computing technology.

关键词： Mobile edge computing ppo algorithm Task offloading GPS-free Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Research on Multi-Objective Optimization Models for Intersection Crossing of Connected Autonomous Vehicles With Traffic Signals

引用

IEEE ACCESS 2024年 12卷 36825-36840页

作者： Ning, Xiaobin Tian, Haoran Lin, Yong Yao, Xueping Hu, Fei Yin, Yuming Zhejiang Univ Technol Coll Mech Engn Hangzhou 310014 Zhejiang Peoples R China Zhejiang Univ Technol Zhijiang Coll Shaoxing 310024 Zhejiang Peoples R China Hozon New Energy Automobile Co Ltd Tongxiang 200062 Zhejiang Peoples R China Zhejiang VIE Sci & Technol Co Ltd Zhuji 311835 Zhejiang Peoples R China

This study delves deeply into the traffic light intersection control issue for connected autonomous vehicles (CAVs). First, we employed the vehicle dynamics simulation software, CarSim, to model the vehicle and utilized the intelligent driving simulation software, PreScan, to establish a road environment model. Subsequently, we designed a rule-based traffic light crossing controller (RB-TLCC) for CAVs in MATLAB/Simulink and implemented a co-simulation using CarSim and PreScan. Furthermore, we conducted driver-in-the-loop studies using the Logitech G27 driving simulator kit and compared the experimental results with those from the RB-TLCC. Our findings indicate that RB-TLCC enhances the regenerative braking energy by 23.5%, improves traffic efficiency by 17.9%, and increases driving smoothness by 50.7%. Additionally, based on Markov Chain theory, we proposed a multi-objective optimization model (MO-OM) for CAVs traffic light intersection crossing using the Proximal Policy Optimization algorithm (ppo). A comparison was conducted under two different operating conditions between RB-TLCC and dynamic programming algorithms (DP). The results indicated that the MO-OM proposed in this study exhibited the best comprehensive control performance among the three control methods. While adhering to traffic regulations, it enhances the recuperation of braking energy, efficiency of vehicle passage, and smoothness of travel at traffic light intersections. This study offers effective methodologies for enhancing the performance of CAVs in traffic light intersection control and holds significant reference value for the advancement of future intelligent transportation systems (ITS).

关键词： Connected autonomous vehicles intelligent traffic system multi-objective optimization optimal speed planning ppo algorithm traffic light intersection control

来源：评论

学校读者我要写书评

暂无评论

An intelligent thermal comfort control strategy for air conditioning of fuel cell vehicles

引用

APPLIED THERMAL ENGINEERING 2024年第PartB期248卷

作者： Hu, Donghai Qiu, Chengyun Lu, Dagang Wang, Jing Huang, Hao Xue, Haitao Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China CATARC Automot Test Ctr Changzhou Co Ltd Changzhou 213161 Peoples R China

Thermal comfort is an important indicator for evaluating vehicle air conditioning, with many influencing factors, but traditional vehicle air conditioning control only take temperature as the control target, neglecting the passenger's actual thermal comfort situation, which cannot automatically adapt to the thermal comfort needs of different passengers. To address this issue, this paper establishes an air conditioning model based on a fuel cell vehicle (FCV) and proposes a control strategy based on thermal comfort, using deep reinforcement learning based on ppo algorithm to establish the control model. Which can automatically control the compressor and blower, this paper conducts simulation analysis under four different environmental conditions which including static and dynamic situations. The results show that when using the intelligent thermal comfort control strategy, the overall thermal sensation values of passengers are 0.006, 0.005, 0.033, and 0.102, respectively;which are 0.088, 0.333, 0.093, and 0.120 lower than the temperature-based control strategy, this proves that the control strategy proposed in this paper can achieve better thermal comfort for passengers.

关键词： Intelligent cockpit Air conditioning control Thermal comfort ppo algorithm

来源：评论

学校读者我要写书评

暂无评论

Research on heterogeneous multi-UAV collaborative decision-making method based on improved ppo

引用

APPLIED INTELLIGENCE 2024年第20期54卷 9892-9905页

作者： Xu, Lin Zhang, Xinmiao Xiao, Dong Liu, Beihong Liu, Aixue Northeastern Univ Coll Informat Sci & Engn Wenhua Rd Shenyang 100190 Liaoning Peoples R China

In order to solve the problem that the Proximal Policy Optimization (ppo) algorithm is difficult to converge in the air-sea battle scenarios with high dynamics, strong interference, and complex state space, the Ray-LAppo algorithm based on Long Short-Term Memory (LSTM) and Attention mechanism is proposed in this paper under the distributed training framework Ray. Firstly, the idea of Centralized Training Distributed Execution (CTDE) is adopted to extend the ppo algorithm to the field of multi-agent and the policy entropy is added to the loss function to encourage the exploration of agents;Secondly, the LSTM network is added to the actor and critic networks to explore the timing relationship between non-independent and identically distributed samples and improve the learning performance of the UAV;In addition, the Attention mechanism is introduced to obtain the states at different time steps and establish a weighted differentiation model of the final value function;Finally, the simulation experiments on the self-developed heterogeneous UAV collaborative decision-making environment show that Ray-LAppo can get the most advanced performance in different scenarios, and also possesses potential value for large-scale real-world applications.

关键词： ppo algorithm LSTM network Attention mechanism Multi-agent coordination Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Cooperative Streaming of Holographic Video Over Wireless Networks: A Proximal Policy Optimization Solution

引用

IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第9期13卷 2387-2391页

作者： Wen, Wanli Yan, Jiping Zhang, Yulu Huang, Zhen Liang, Liang Jia, Yunjian Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China

Adapting holographic video streaming to fluctuating wireless channels is essential to maintain consistent and satisfactory Quality of Experience (QoE) for users, which, however, is a challenging task due to the dynamic and uncertain characteristics of wireless networks. To address this issue, we propose a holographic video cooperative streaming framework designed for a generic wireless network in which multiple access points can cooperatively transmit video with different bitrates to multiple users. Additionally, we model a novel QoE metric tailored specifically for holographic video streaming, which can effectively encapsulate the nuances of holographic video quality, quality fluctuations, and rebuffering occurrences simultaneously. Furthermore, we formulate a formidable QoE maximization problem, which is a non-convex mixed integer nonlinear programming problem. Using proximal policy optimization (ppo), a new class of reinforcement learning algorithms, we devise a joint beamforming and bitrate control scheme, which can be wisely adapted to fluctuations in the wireless channel. The numerical results demonstrate the superiority of the proposed scheme over representative baselines.

关键词： Streaming media Quality of experience Bit rate Video recording Quality assessment Wireless networks Fluctuations Holographic video streaming beamforming bitrate control ppo algorithm QoE

来源：评论

学校读者我要写书评

暂无评论

A Time-Driven Dynamic Weapon Target Assignment Method

引用

IEEE ACCESS 2023年 11卷 129623-129639页

作者： Liu, Chang Li, Jiang Wang, Ye Yu, Yang Guo, Lihong Gao, Yuan Chen, Yang Zhang, Feng Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Aviat Univ Air Force Sch Aviat Operat & Serv Changchun 130022 Peoples R China

The traditional dynamic weapon target assignment model is a combination of multiple static weapon target assignment stages. The assignment of the next stage is carried out after the result of the previous static weapon target assignment is settled. However, between two static weapon target assignment stages, the threat ranking of multiple targets may change with time, and the traditional dynamic weapon target assignment model does not take this time issue into consideration. This paper proposes a "time sampling dynamic weapon assignment model". This model divides the decision-making stage by setting the time interval of data collection, and it can capture the real-time changes in the target threat degree and make timely decisions. With this model, this study designed a dynamic weapon target assignment method based on the reinforcement learning algorithm. Additionally, according to this method, a comparative experiment with different sampling time divisions was designed, and a better sampling time division method was obtained. Finally, a comparative experiment between the reinforcement learning algorithm and the traditional heuristic algorithm was designed in this study. The simulation results show that, compared with the traditional heuristic algorithm, the proposed assignment model and the reinforcement learning algorithm are better in terms of decision-making timeliness and global considerations.

关键词： Heuristic algorithms Missiles Discrete wavelet transforms Reinforcement learning Computational modeling Optimization Real-time systems Weapons Object detection Dynamic weapon target assignment simulation model reinforcement learning heuristic algorithm ppo algorithm

来源：评论

学校读者我要写书评

暂无评论

FMI real-time co-simulation-based machine deep learning control of HVAC systems in smart buildings: Digital-twins technology

引用

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第4期45卷 661-673页

作者： Mohseni, Saeid-Reza Zeitouni, Meisam Jahanshahi Parvaresh, Ahmad Abrazeh, Saber Gheisarnejad, Meysam Khooban, Mohammad-Hassan Shiraz Univ Shiraz Iran Sharif Univ Technol Tehran Iran Shahid Bahonar Univ Kerman Kerman Iran Aarhus Univ Dept Elect & Comp Engn Aarhus Denmark Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia

As heating, ventilation, and air conditioning (HVAC) systems have become one of the most contributing systems in energy consumption in the world, the control of these large-scale systems remains a challenging duty due to the decoupling effects of control variables. Accordingly, the penetration of these types of systems in all-smart buildings has increased in recent years. Furthermore, the application of digital twin as a fast-growing concept is being developed. In HVAC systems, independent and accurate control of temperature and humidity of the indoor air has been playing an undeniable role in reducing energy consumption. In this paper, to have cost-effective energy management in a single-zone HVAC system, a new reliable digital twin proximal policy optimization (ppo)-based model-independent nonsingular terminal sliding-mode control (MINTSMC) methodology has been proposed. Moreover, due to the nonlinear characteristics of HVAC systems, MINTSMC tends to handle the un-modeled system dynamics and disturbances. For regulating parameters of proposed control, an efficient ppo algorithm has been developed due to its actor-critic-based reinforcement learning. Extensive examinations and comparative analyses with particle swarm optimization designed sliding-mode control and proportional-integral-derivative controller have been made using digital twin of the proposed controller to show the importance, accuracy, and application of this method in the comfort and energy management achievement of HVAC control systems. A digital signal processor computing device has been utilized for implementation by utilizing hardware-in-loop (HIL) in the concept of the digital twin. To determine the interface between established models, software-in-loop, and HIL, the Functional Mock-up Interface has been utilized. The outcomes revealed a superior performance of suggested digital twin-based controller than the compared control methodologies in the compensation of unknown uncertainties, fast-track

关键词： HVAC system ppo algorithm nonsingular terminal sliding-mode controller hardware-in-loop digital twin (DT)

来源：评论

学校读者我要写书评

暂无评论

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

请选择保存的检索档案：

请选择收藏分类：

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

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

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：