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43rd Chinese control Conference, CCC 2024

作者： Li, Shilong Du, Shengli Wu, Di Gao, Yongfeng Beijing University of Technology School of Artificial Intelligence and Automation Beijing100124 China Beijing Laboratory of Smart Environmental Protection Beijing100124 China Dalian University of Technology School of Control Science and Control Engineering Dalian116024 China

ISBN: (纸本)9789887581581

This paper is concerned with the routing problem of a mixed vehicle fleet composed by both electric and conventional vehicles. A bi-objective model with the aim of minimizing carbon emissions and maximising customer satisfaction is established. To further tackle the challenges posed by the distinct constraints of electric and conventional vehicles, a partial variable-length coding method is introduced to facilitate chromosome generation in the model. Then, a novel NSGA-II including the above designed coding method is proposed for solving the optimization problem. By comparing the experiments results of solving separate single-objective optimization and the two conflict optimization objectives simultaneously, it is verified that the proposed encoding approach and algorithm is effective. The results suggest that the bi-objective model is well-established, demonstrating the anticipated conflict between the two objectives. Additionally, the partial variable-length coding method is shown to be effective in achieving chromosome generation tasks. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Vehicle routing

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Multi-AAV Energy-Efficient Detection Coverage Under Jamming Environment: A Hierarchical Collaborative Learning Approach

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7351-7363页

作者： Chao Fang Yanxiang Feng Xiaoling Li Yikang Yang School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China School of Electronics and Control Engineering Chang'an University Xi'an China

Autonomous aerial vehicles (AAVs) can provide detection coverage service in many scenarios. The fair coverage is achieved by designing carefully AAVs' trajectories, which are established at each step by choosing the moving action and channel access for transmitting data. However, under the jamming environment, the problematic trajectories could lead to mutual interference and malicious jamming, such that the coverage service fails. The selections of moving action and channel access are usually coupled, and so far no work has addressed them jointly for planning trajectory. As such, this paper investigates the multi-AAV joint optimization of moving action and channel access for the energy-efficient detection coverage. To decouple the strongly-coupled moving action and channel access, we model the studied optimization problem as a hierarchical game, where the stochastic game (resp. potential game) is applied for selecting moving action (resp. channel access). Then, we propose a multi-agent hierarchical cooperative learning (MAHCL) algorithm to attain near-optimal solution for the joint optimization. It is proved that the proposed MAHCL algorithm can asymptotically converge to the near-optimal joint strategy with lower computational complexity. Finally, the simulation results show the higher energy efficiency of MAHCL algorithm compared with the benchmarks.

关键词： Jamming Autonomous aerial vehicles Trajectory Interference Games Heuristic algorithms Optimization Energy efficiency Dynamic scheduling Clustering algorithms

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Swarm-Based Dynamic Coverage of Multi-ASV Systems in the Presence of Measurement Noises

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7237-7250页

作者： Liu, Lu Jiao, Shijian Han, Bing Li, Tieshan Peng, Zhouhua Dalian Maritime University School of Marine Electrical Engineering Dalian116026 China Dalian Key Laboratory of Swarm Control and Electrical Technology for Intelligent Ships Dalian116026 China State Key Laboratory of Maritime Technology and Safety Dalian116026 China Shanghai Ship and Shipping Research Institute National Engineering Research Center of Ship & Shipping Control System Shanghai200135 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China

This paper is concerned with the dynamic coverage of multiple autonomous surface vehicles (ASVs) in the presence of static and dynamic obstacles, limited communication distance, and measurement noises. A modular control method including planning, guidance, and control is proposed. At the planning level, a swarm-based dynamic coverage strategy is developed to achieve the coverage of a given domain. Both the collision avoidance and connectivity preservation issues are solved by using artificial potential fields. At the kinematic level, novel adaptive line-of-sight guidance laws are presented based on cascade extended state observers (CESOs), such that accurate guidance signals are given by compensating for time-varying sideslip angle and suppressing measurement noises. At the kinetic level, two CESOs are developed to estimate the internal model uncertainties and external environmental disturbance, and antidisturbance kinetic control laws are designed subsequently. The proposed CESOs overcome the noises sensitivity problem of classical observers, and thus facilitate the practical implementation under measurement noises. The input-to-state stability of the closed-loop system is confirmed by the Lyapunov theory and cascade stability theory. Simulation and hardware-in-the-loop experiment results are provided to demonstrate the effectiveness of the proposed swarm-based dynamic coverage method. © 1967-2012 IEEE.

关键词： Velocity measurement

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Incentive Design in Noncooperative Dynamical Systems for Social Welfare Maximization Using Limited Payoff Information 63

Incentive Design in Noncooperative Dynamical Systems for Soc...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Yan, Yuyue Ye, Maojiao Hashimoto, Kazumune Wu, Yuhu Kong, He Osaka University Graduate School of Engineering Suita565-0871 Japan Nanjing University of Science and Technology School of Automation Nanjing210094 China Dalian University of Technology School of Control Science and Engineering Dalian116024 China Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen China

ISBN: (纸本)9798350316339

In this paper, an incentive mechanism is developed to maximize the weighted social welfare for pseudo-gradient-based noncooperative dynamical systems. In the proposed approach, the system manager manipulates agents' decision making by collecting taxes and giving subsidies to the agents under a sustainable budget constraint using the partial knowledge related to the payoff structure from the agents except a secret agent. To avoid direct acquisition of the payoff information of all the agents, our idea is to shift the stationary points of those partial agents and hence indirectly move the Nash equilibrium of the entire system. Sufficient conditions are derived under which the Nash equilibrium is shifted towards the target state achieving Pareto efficiency under the incentive mechanism without knowing any payoff information of the secret agent. Some convergent conditions to ensure agents' state converge to the target state under a sustainable budget are provided for the given initial state. We present a numerical example to illustrate the efficacy of our results. © 2024 IEEE.

关键词： Budget control

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A Method for Evaluating the Observation Effectiveness of Microwave Remote Sensing Satellite Constellation 7th

A Method for Evaluating the Observation Effectiveness of ...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhang, Tianyao Zhang, Chi Xiao, Jin Wang, Hong China Academy of Space Technology Beijing100094 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Institute of Control Engineering Beijing100094 China

ISBN: (纸本)9789819733316

Satellites tend to form a constellation for better performance over a region of thousands of kilometers. However, how to set up the configuration of the constellation is still a challenge for an engineer. This article proposes several statistical-based metrics to evaluate the effectiveness of constellations from the perspective of observation applications. Then, this article proposes a software tool to analyze the metrics of constellations over a target area. Furthermore, this article illustrates the proposed method with the help of a 40-satellite case study. Finally, this article concludes the proposed method and discusses future work. This method of evaluation for remote sensing constellations would help the engineer determine the design parameters of the large constellation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Satellites

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Multiple Uncertainty Rejection-Based Power Lifting Adaptive Fixed-Time Tracking control 13

Multiple Uncertainty Rejection-Based Power Lifting Adaptive ...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Liu, Yang Ren, Hongru Hui, Yu Li, Hongyi College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao266061 China School of Automation and Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9798350361674

This work investigates an adaptive fixed-time tracking control issue of nonlinear systems with the multiple uncertainty. A practically fixed-time lemma is established, and the singularity problem of fixed-time tracking control is handled by exploiting the power lifting technique. Moreover, a nonlinear fixed-time tracking scheme is proposed under the framework of the backstepping, and a simple design process is presented, which can simultaneously tackle uncertain parameters and disturbances. Finally, the numerical simulation is to explain the effectiveness of the proposed method. © 2024 IEEE.

关键词： Backstepping

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Deep Learning Method for Leakage Location Detection of Pneumatic Systems Based on Infrared Thermal Image Evaluation 24

Deep Learning Method for Leakage Location Detection of Pneum...

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7th International Conference on Software engineering and Information Management, ICSIM 2024

作者： Chang, Jiaqi Shi, Yan Yang, Liman Niu, Yanxia Nie, Yulong Yang, Zhiguo Li, Lei Zhang, Wenchao School of Automation Science and Electrical Engineering Beihang University China Engineering Training Center Beihang University China Guangzhou Industrial Control Environmental Protection Technology Co. Ltd China

ISBN: (纸本)9798400709197

Pneumatic systems are an essential fluid transmission method in the industrial field, which can achieve the transmission and control of power or signals. Leakage in pneumatic systems is challenging to detect and is a highly harmful fault. Most existing leak detection methods use flow or pressure sensors to detect specific leakage amounts, while leak localization is still in the traditional manual detection, which significantly restricts localization efficiency. This article establishes a deep learning framework-based thermal image localization method for pneumatic system leak detection. Infrared images captured in the pneumatic system were collected, and then a deep-learning localization method based on the YOLO framework was established. Then, the recognition accuracy of this method was calculated. The results indicate that the accuracy has reached 99.5% of mAP_0.5 and 86.27% of mAP_0.5:0.95, indicating that this work is a meaningful study that can apply intelligent computing to engineering. © 2024 ACM.

关键词： Object detection

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Optimal Strategy for Aircraft Pursuit-evasion Games via Self-play Iteration

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Machine Intelligence Research 2024年第3期21卷 585-596页

作者： Xin Wang Qing-Lai Wei Tao Li Jie Zhang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100049China Institute of Systems Engineering Macao University of Science and TechnologyMacao999078China

In this paper,the pursuit-evasion game with state and control constraints is solved to achieve the Nash equilibrium of both the pursuer and the evader with an iterative self-play *** the condition where the Hamiltonian formed by means of Pontryagin’s maximum principle has the unique solution,it can be proven that the iterative control law converges to the Nash equilibrium ***,the strong nonlinearity of the ordinary differential equations formulated by Pontryagin’s maximum principle makes the control policy difficult to figured *** the system dynamics employed in this manuscript contains a high dimensional state vector with *** practical applications,such as the control of aircraft,the provided overload is ***,in this paper,we consider the optimal strategy of pursuit-evasion games with constant constraint on the control,while some state vectors are restricted by the function of the *** address the challenges,the optimal control problems are transformed into nonlinear programming problems through the direct collocation ***,two numerical cases of the aircraft pursuit-evasion scenario are given to demonstrate the effectiveness of the presented method to obtain the optimal control of both the pursuer and the evader.

关键词： Differential games pursuit-evasion games nonlinear control optimal control Nash equilibrium solution

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Linear Regression-Based Autonomous Intelligent Optimization for Constrained Multiobjective Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4620-4634页

作者： Wang, Yan Sun, Xiaoyan Zhang, Yong Gong, Dunwei Hu, Hejuan Zuo, Mingcheng China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Qingdao University of Science and Technology College of Automation and Electronic Engineering Shandong Qingdao266061 China China University of Mining and Technology Artificial Intelligence Research Institute Jiangsu Xuzhou221116 China

It is very challenging to autonomously generate algorithms suitable for constrained multiobjective optimization problems due to the diverse performance of existing algorithms. In this article, we propose a linear regression (LR)-based autonomous intelligent optimization method. It first extracts typical features of a constrained multiobjective optimization problem by focused sampling to form a feature vector. Then, a LR model is designed to learn the relationship between optimization problems and intelligent optimization algorithms (IOAs). Finally, the trained model autonomously generates a suitable IOA by inputting the feature vector. The proposed method is applied to six constrained multiobjective benchmark test sets with various characteristics and compared with seven popular optimization algorithms. The experimental results verify the effectiveness of the proposed method. In addition, the proposed method is used to solve the operation optimization problems of an integrated coal mine energy system, and the experimental results show its practicability. © 2020 IEEE.

关键词： Feature extraction

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A Proposal Of An Iterative Learning control Method For Simulating A Flyback Converter Using PSCAD 9

A Proposal Of An Iterative Learning Control Method For Simul...

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9th International Conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Cao, Trung Thanh Van, Cuong Trinh Van, Hung Pham Nguyen, Minh Quang Hoang, Anh Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi Viet Nam Ha Noi University of Industry Department of Automation and Control Hanoi Viet Nam

ISBN: (纸本)9798331505042

DC-DC converters are essential components in power supply applications, portable electronics, renewable energy systems, and various other industries. An accurate system model is crucial for the development of the controller for a DC-DC converter. This research study focused on the development of an iterative learning control that operates without requiring the system model. The study employs PSCAD simulation software to simulate and analyze the effectiveness of an iterative learning control and a sliding mode controller for flyback converter. Detailed simulation results and comparisons between the two methodologies will be presented. © 2024 IEEE.

关键词： Sliding mode control

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