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32nd Chinese control and Decision Conference, CCDC 2020

作者： Nan, DIng Maria, Akira Xianghua, Meng Nasibullah Wu, Di Dalian University of Technology Department of Computer Science and Technology Dalian116024 China Dalian University of Technology Department of Control Theory and Control Engineering Dalian116024 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Dalian University of Technology Dalian Liaoning116024 China

ISBN: (纸本)9781728158549

Despite the unavailability of intersections and turns, expressway has several lane closures and ramps. Mandatory lane change takes place when the geographical condition requires the vehicles to shift lane, such as, exits, on and off-ramps. Due to a high-speed regulation on the expressway, it is more difficult and riskier to shift lane, therefore better strategies for semi-automation vehicles have to be implemented for the users' safety. This paper studies the efficiency of the Stackelberg based algorithm for safer mandatory lane maneuver, yet still with appropriate speed. The paper also utilizes model predictive controller (MPC) to predict the motions of surrounding vehicles a few time steps in the future to model the probabilities of subject vehicles to shift. Then we consider the costs, MPC and the game to output the best time for subject vehicle to shift. Finally, we simulate the algorithm to determine if it is efficient by comparing it to other existing algorithms and breaking the simulation down to various indicators. © 2020 IEEE.

关键词： Game theory

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An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments

arXiv

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arXiv 2022年

作者： Han, Zhichao Wu, Yuwei Li, Tong Zhang, Lu Pei, Liuao Xu, Long Li, Chengyang Ma, Changjia Xu, Chao Shen, Shaojie Gao, Fei State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community.1 Copyright © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Distributed control of Multiple-Bus Microgrid With Paralleled Distributed Generators

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 676-684页

作者： Bo Fan Jiangkai Peng Jiajun Duan Qinmin Yang Wenxin Liu IEEE the State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University the Smart Microgrid and Renewable Technology (SMRT) Research Laboratory Department of Electrical and Computer Engineering Lehigh University the Global Energy Interconnection Research Institute North America (GEIRINA)

A microgrid is hard to control due to its reduced inertia and increased uncertainties. To overcome the challenges of microgrid control, advanced controllers need to be *** this paper, a distributed, two-level, communication-economic control scheme is presented for multiple-bus microgrids with each bus having multiple distributed generators(DGs) connected in parallel. The control objective of the upper level is to calculate the voltage references for one-bus subsystems. The objectives of the lower control level are to make the subsystems' bus voltages track the voltage references and to enhance load current sharing accuracy among the local DGs. Firstly, a distributed consensusbased power sharing algorithm is introduced to determine the power generations of the subsystems. Secondly, a discrete-time droop equation is used to adjust subsystem frequencies for voltage reference calculations. Finally, a Lyapunov-based decentralized control algorithm is designed for bus voltage regulation and proportional load current sharing. Extensive simulation studies with microgrid models of different levels of detail are performed to demonstrate the merits of the proposed control scheme.

关键词： Coordinate control decentralized control multiple-bus microgrid paralleled distributed generations power sharing algorithm

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Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

arXiv

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arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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S2R-CMI: A Robust Deep Reinforcement Learning method based on counterfactual estimation and state importance evaluation under additive noise disturbance

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Neurocomputing 2025年 648卷

作者： Zhenyuan Chen Zhi Zheng Wenjun Huang Xiaomin Lin College of Computer and Cyber Security Fujian Normal University FuZhou 350117 China College of Control Science and Engineering Zhejiang University Hangzhou 310027 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China

The development of Deep Reinforcement Learning (DRL) overcomes the limitations of traditional reinforcement learning in discrete spaces, extending its applications to scenarios in continuous spaces. However, disturbance commonly encountered in real-world environments poses significant threats to the performance of DRL algorithms, potentially leading to erroneous decision-making by agents and severe consequences. To address this issue, this paper proposes a Robust Deep Reinforcement Learning (RDRL) method named S2R-CMI, which aims to mitigate the impact of additive noise in the state space on DRL performance without requiring prior knowledge of the noise disturbance. Specifically, a state-based and reward-based conditional mutual information mechanism is designed to dynamically capture state importance and estimate its contribution to rewards. To address the lack of counterfactual data during training, a counterfactual label estimation method is proposed to approximate the counterfactual reward distribution while avoiding local optima during network training. State importance is then evaluated to quantify the impact of disturbance on states. Finally, we validate the proposed method in five scenarios: Cartpole, LunarLander-Discrete, LunarLander-Continuous, Build-Marine, and Half-Cheetah, under state disturbance. Experimental results demonstrate that S2R-CMI significantly enhances the robustness of DRL algorithms. Furthermore, we conduct experiments in some scenarios without state disturbance, and the results indicate that the method also achieves strong performance, further verifying its superiority and generalization capabilities.

关键词： Additive noise Conditional mutual information Robust deep reinforcement learning State disturbance

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Cache-Assisted Content Delivery for NOMA-Based Satellite-Edge-Terminal Cooperation Networks

Cache-Assisted Content Delivery for NOMA-Based Satellite-Edg...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Chao Fang Hang Xu Yingshan Li Haofei Xie Shu-Ming Tseng Zheng Yang Deze Zeng Faculty of Information Technology Beijing University of Technology Beijing China Purple Mountain Laboratories Nanjing China Key Laboratory of Industrial Internet of Things & Networked Control Chongqing University of Posts and Telecommunications Chongqing China Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Normal University Fuzhou China School of Computer Science China University of Geosciences Wuhan China

To satisfy differentiated service requirements of delay-sensitive and computing-intensive tasks in satellite communication networks, we propose a cache-assisted low-latency content distribution scheme in multi-cell multi-carrier nonorthogonal multiple access (MCMC-NOMA)-based satellite-edge-terminal cooperation environments. In the paper, a delay minimization problem is formulated to achieve optimal content distribution by jointly optimizing the allocation of subchannels and transmit power, which is decomposed and solved in each slot by using many-to-one matching and successive convex approximation, respectively. Simulation results show that the proposed model significantly improve network latency and content distribution in comparison to its existing counterparts in cloud-edge-terminal cooperation networks.

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Risk Identification Method for Smuggled Frozen Meat in Urban Public Safety Awareness and Early Warning

Risk Identification Method for Smuggled Frozen Meat in Urban...

引用

Intelligent Communication and Networking (ICN), International Conference on

作者： Xueru Li Yan Zhang Lingjie Liu Ye Tian Yue Zhang College of Computer Science and Technology Harbin Engineering University Harbin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Hangzhou Institute of Technology Xidian University Hangzhou China Key Laboratory of Peace-Building Big Data of Zhejiang Province Zhejiang China

With the continuous growth in demand for meat and frozen products in the cold chain market, illegally smuggled frozen goods have quickly entered the market due to their low prices, leading to a series of food safety issues. This article proposes a method for identifying the risks of smuggled frozen meat for urban public safety awareness using the YOLOv7 model. The YOLOv7 model is used to detect data captured at various traffic checkpoints, obtaining spatiotemporal information of frozen goods vehicles. This information is then combined with map data to generate vehicle trajectory information. The trajectory similarity between the frozen vehicle trajectories and the specialized database of frozen transportation routes is analyzed using the EDwP distance algorithm. The EDwP algorithm employs a threshold-free approach, dynamically interpolating using the concept of projection, and automatically adapting to non-uniform sampling rates to address the issue of inconsistent trajectory data sampling rates. The experimental results of the YOLOv7-EDwP algorithm were verified by verifying the alerts generated in the recent period through on-site verification by a third-party inspection agency, with a recognition accuracy of 90.77%. This holds significant significance in the detection and warning of smuggled frozen meat.

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Elliptic Target Positioning Based on Balancing Parameter Estimation and Augmented Lagrange Programming Neural Network

SSRN

引用

SSRN 2022年

作者： Xiong, Wenxin Liang, Junli Wang, Zhi So, Hing Cheung Department of Computer Science University of Freiburg Freiburg79110 Germany School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Department of Electrical Engineering City University of Hong Kong Hong Kong

Elliptic positioning (EP) has been receiving increasing attention in recent years with the development of multistatic systems. This article considers mitigating the negative effects of biased measurements on the location estimation performance of EP, by introducing a balancing parameter into the traditional non-outlier-resistant least squares type formulation. The resulting problem is then solved by exploiting the augmented Lagrange programming neural network (ALPNN), which is a generally applicable and asymptotically stable nonlinear constrained neurodynamic optimization framework. Moreover, the Cram\'{e}r-Rao lower bound for EP in non-Gaussian noise is derived. The superiority of the proposed ALPNN approach over a number of existing EP estimators is demonstrated through computer simulations. © 2022, The Authors. All rights reserved.

关键词： Parameter estimation

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A Novel Fuzzy Model Identification Approach Based on FCM and Gaussian Membership Function

A Novel Fuzzy Model Identification Approach Based on FCM and...

引用

Chinese control Conference (CCC)

作者： Yaxue Ren Jinfeng Lv Fucai Liu Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

To solve the problem of fuzzy identification of nonlinear systems, a novel fuzzy identification method based on fuzzy c-means clustering (FCM) algorithm and Gaussian function is proposed. Firstly, fuzzy clustering algorithm is used to divide the input space to obtain the clustering center, then the clustering center is used as the gaussian function center to determine the membership function to obtain the premise parameters of the fuzzy model, and the conclusion parameters of the fuzzy model are identified by recursive least squares (RLS). Finally, three simulation examples are given to verify the effectiveness of the proposed method in identifying T-S fuzzy model.

关键词： Mathematical model Clustering algorithms Matrices Input variables Marine vehicles Partitioning algorithms MISO communication

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Expression recognition algorithm based on local and global feature reconstruction residuals

Expression recognition algorithm based on local and global f...

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International Conference on Information Science and control engineering (ICISCE)

作者： Yaqian Li Ya Lei Chao Wu Haibin Li Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

In this paper, an algorithm is proposed to improve expression recognition by combining reconstructed residuals of local and global information. Firstly, positional relation is introduced into local binary features encoding, and a non-negative matrix decomposition method with sparse correlation constraints is employed to perform reducing of dimension. Then, the features are encoded by bag-of-words model to form local features. Secondly, a discrete wavelet transform is used to emphasis on expression information and extract expression-enhanced global features. Thirdly, collaboration Represents is used to calculate local partial reconstruction residuals and is combined with global reconstruction residuals to construct the final residual to be classified at the decision level. Experiments on Jaffe, TEFID and CK+ data sets prove the effectiveness of the proposed algorithm.

关键词： Information science Correlation control engineering Transforms Feature extraction Encoding Classification algorithms

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