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Optimal Tracking control for Leader-Following Consensus of Nonlinear Multiagent Systems

IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3098-3107页

作者： Su, Wei Mu, Chaoxu Yang, Xiong Bian, Jinshan Sun, Changyin Tianjin University School of Control Science and Engineering Tianjin300072 China Anhui University Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Hefei230601 China Anhui University School of Artificial Intelligence Hefei230000 China Southeast University School of Automation Nanjing210096 China

This article investigates distributed adaptive leader-following consensus tracking optimal control problem for nonlinear multiagent systems (MASs) subject to unknown nonlinearities and uncertain external disturbances. In contrast to traditional centralized control, the primary challenge is that only partial subsystems can access the desired reference trajectory. To address this, positive time-varying smooth function compensating terms are introduced to counteract the effects of uncertain external disturbances and unknown desired trajectories. Then, by fusing consensus errors into the backstepping technique, feedforward controllers are given. On this basis, the controlled nonlinear systems are transformed into an equivalent affine form, and feedback optimal controllers are designed using actor and critic neural networks (NNs) to execute control behavior and evaluate control performance. The whole control laws comprise both feedforward and feedback controllers. The proposed distributed adaptive consensus control protocol can simultaneously achieve desired optimal control performance and minimize the cost function, as demonstrated through theoretical analysis and simulation results. © 2025 IEEE.

关键词： Cost functions

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A new noise network and gradient parallelisation‐based asynchronous advantage actor‐critic algorithm

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IET Cyber-Systems and Robotics 2022年第3期4卷 175-188页

作者： Zhengshun Fei Yanping Wang Jinglong Wang Kangling Liu Bingqiang Huang Ping Tan Provincial Key Institute of Robotics School of Automation and Electrical EngineeringZhejiang University of Science and TechnologyHangzhouChina State Key Lab of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhouChina

Asynchronous advantage actor‐critic(A3C)algorithm is a commonly used policy opti-mization algorithm in reinforcement learning,in which asynchronous is parallel inter-active sampling and training,and advantage is a sampling multi‐step reward estimation method for computing *** order to address the problem of low efficiency and insufficient convergence caused by the traditional heuristic exploration of A3C algorithm in reinforcement learning,an improved A3C algorithm is proposed in this *** this algorithm,a noise network function,which updates the noise tensor in an explicit way is constructed to train the *** advantage estimation(GAE)is also adopted to describe the dominance ***,a new mean gradient parallelisation method is designed to update the parameters in both the primary and secondary networks by summing and averaging the gradients passed from all the sub‐processes to the main *** experiments were conducted in a gym environment using the PyTorch Agent Net(PTAN)advanced reinforcement learning library,and the results show that the method enables the agent to complete the learning training faster and its convergence during the training process is *** improved A3C algorithm has a better performance than the original algorithm,which can provide new ideas for sub-sequent research on reinforcement learning algorithms.

关键词： asynchronous advantage actorcritic (A3C) generalised advantage estimation (GAE) parallelisation reinforcement learning

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A Data-Driven Adaptive Optimal control with Input Saturation for Driverless Heavy-Duty Truck

A Data-Driven Adaptive Optimal Control with Input Saturation...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Ji, Honghai Yang, Jianfeng Liu, Shida Fan, Lingling Wang, Li School of Electrical and Control Engineering North China University of Technology Beijing100144 China School of Automation Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9789819639687

This paper mainly studies the longitudinal speed tracking problem of driverless heavy-duty trucks based on data-driven methods. A data-driven adaptive optimal control algorithm with control input saturation constraints is proposed in this paper to solve the problems of control input saturation and difficulty in establishing an accurate model in driverless heavy-duty truck systems. First, control constraints are added to the control input saturation problem of driverless heavy-duty trucks. Then, the dynamic linearization technology is used to model the nonlinear system of driverless heavy-duty trucks. After that, an appropriate index function is selected and the Newton-type optimization method is used to obtain the optimal controller. The method proposed in this paper only uses a small amount of input and output data and does not need to establish an accurate mathematical model. The simulation results show that compared with the PID algorithm, the proposed algorithm has advantages in speed tracking when applied to driverless heavy-duty truck systems. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Automobiles

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Intelligent obstacle avoidance algorithm for safe urban monitoring with autonomous mobile drones

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Journal of Electronic Science and Technology 2024年第4期22卷 19-36页

作者： Didar Yedilkhan Abzal E.Kyzyrkanov Zarina A.Kutpanova Shadi Aljawarneh Sabyrzhan K.Atanov Department of Computer Engineering Astana IT UniversityAstana010000Kazakhstan Department of Computer and Software Engineering L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Automation and Control Systems L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Software Engineering Jordan University of Science and TechnologyIrbid22110Jordan

The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone *** deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition *** this context,this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring *** primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles,such as *** algorithm incorporates three key components to optimize the obstacle detection,navigation,and energy efficiency within a drone ***,the algorithm utilizes a method to calculate the position of a virtual leader,acting as a navigational beacon to influence the overall direction of the ***,the algorithm identifies observers within the swarm based on the current *** further refine obstacle avoidance,the third component involves the calculation of angular velocity using fuzzy *** approach considers the proximity of detected obstacles through operational rangefinders and the target’s location,allowing for a nuanced and adaptable computation of angular *** integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically,ensuring practical obstacle *** proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive *** results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.

关键词： Drone swarms Fuzzy logic Intelligent solution Smart city Urban monitoring

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Lidar-Visual-Inertial Odometry with Online Extrinsic Calibration

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 70-76页

作者：茅天阳赵文韬王景川陈卫东 Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China

To achieve precise localization,autonomous vehicles usually rely on a multi-sensor perception system surrounding the mobile *** is a time-consuming process,and mechanical distortion will cause extrinsic calibration ***,we propose a lidar-visual-inertial odometry,which is combined with an adapted sliding window mechanism and allows for online nonlinear optimization and extrinsic *** the adapted sliding window mechanism,spatial-temporal alignment is performed to manage measurements arriving at different *** nonlinear optimization with online calibration,visual features,cloud features,and inertial measurement unit(IMU)measurements are used to estimate the ego-motion and perform extrinsic *** experiments were carried out on both public datasets and real-world *** indicate that the proposed system outperforms state-of-the-art open-source methods when facing challenging sensor-degenerating conditions.

关键词： autonomous vehicles multi-sensor fusion online calibration

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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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Positioning Method for Covered Vehicles Based on Neutrino Detection and Reinforcement Learning 3

Positioning Method for Covered Vehicles Based on Neutrino De...

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3rd International Conference on Electronic Information engineering and Computer, EIECT 2023

作者： Han, Mingren Gao, Jiashan Zhou, Peng Zhang, Yixin Beijing Institute of Control Engineering China Academy of Space Technology Beijing China Beihang University School of Automation Science and Electrical Engineering Beijing China

ISBN: (纸本)9798350357707

In complex environments like underwater or underground, traditional navigation technologies face limitations due to the restricted penetration of optical and radio signals. Neutrino signals, which have extremely strong penetration capabilities, offer a new possibility. This study explores the challenges of navigation and positioning of covered vehicles and proposes a neutrino-based navigation and positioning technology to overcome the limitations of traditional navigation technologies in complex environments. This research combines reinforcement learning methods and Kalman filters, using the Proximal Policy Optimization (PPO) algorithm to tune the parameters of the Kalman filter to improve navigation and positioning accuracy. In the study, a Kalman filter estimation system model for vehicles in navigation signal denied environment environments was first established, then a navigation and positioning method based on neutrino detection was designed, and finally, a parameter adaptive PPO Extended Kalman Filter (PPOEKF) state estimation method was proposed. Through mathematical simulation and comparative analysis, the research results show that using reinforcement learning methods to tune the parameters of the Kalman filter can improve the accuracy of navigation and positioning, especially in complex environments. © 2023 IEEE.

关键词： Reinforcement learning

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Improved control Strategies for Totem-Pole PFC With True Full Range ZVS Operation

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IEEE Transactions on Industrial Electronics 2023年第3期70卷 2419-2430页

作者： Chen, Jie Tai, Weiyu Xun, Benxin Gong, Chunying Chen, Jiawei Nanjing University of Aeronautics and Astronautics Center for More-Electric-Aircraft Power System Ministry of Industry and Information Technology College of Automation Engineering Nanjing211106 China Chongqing University Key Laboratory of Complex System Safety and Control Ministry of Education College of Automation Chongqing400044 China

Zero voltage switching (ZVS) of power semiconductor devices is an effective way to improve the efficiency and power density of the power electronic converters with extremely high switching frequency. This article aims to analyze and realize the true full-range ZVS operation of a totem-pole power factor correction circuit. The available ZVS control strategies are investigated, and the conditions for the realization of full-range ZVS operation are analyzed. It is revealed that the existed control strategies did not consider the influence of the dead-time, resulting in the failure of ZVS operation in some input voltage ranges. To address this issue, two feasible retrofitted control strategies, namely the turn-off current optimization control and the adaptive dead-time control, are proposed. The merits and drawbacks of the two control strategies are carefully analyzed and compared. It is found that the adaptive dead-time control can not only achieve the true full range ZVS but also significantly reduce the loss of the freewheeling diodes. The design consideration of key parameters is discussed. Finally, the feasibility of the proposed control and the correctness of the theoretical analyses are verified by simulation and experiment. © 1982-2012 IEEE.

关键词： Zero voltage switching

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Fractal Converters with Boundless Current and Voltage Ratings 3

Fractal Converters with Boundless Current and Voltage Rating...

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3rd International Conference on Power Systems and Electrical Technology, PSET 2024

作者： Fang, Jingyang Jiang, Qin Yang, Hanqing Shandong University School of Control Science and Engineering Jinan China Sichuan University College of Electrical Engineering Chengdu China University of Electronic Science and Technology of China School of Automation Chengdu China

ISBN: (纸本)9798350353204

Extremely high current/voltage stresses challenge power electronics. This paper proposes a novel series of power converters, collectively referred to as Fractal (F) converters. F converters consist of several identical layers, which further comprise many identical cells or submodules. Salient benefits of F converters include but not limited to 1) modularity and scalability;2) boundless current and voltage ratings;3) high power quality;4) unified control applied to various layers;5) high reliability;6) control flexibility, e.g., for efficiency optimization. F converters will quickly find their applications in 1) high-voltage dc/ac transmissions;2) renewable energy generation;3) power quality enhancement equipment;4) electric vehicles;5) marine and aircraft propulsion;6) medical applications such as noninvasive magnetic stimulators;7) high-frequency high-power power supplies;8) plasma applications;9) future distributed power grids. For validations, simulation results are presented, demonstrating that F converters operate well as high-voltage and high-current inverters as well as arbitrary pulse generators. © 2024 IEEE.

关键词： Power converters

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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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