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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Yao, Xinyu Zhao, Qingjie Lv, Xingchen Wang, Lei Liu, Wangwang Beijing Institute of Technology School of Computer Science & Technology Beijing China Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9798350363173

Being able to safely land on the surface is one of the primary challenges when a probe exploring an asteroid. In order to ensure landing safety, the landing location planning needs to comprehensively consider the terrain characteristics and avoid obstacles such as protruding rocks, craters and slopes. Relatively flat areas are suitable for landing. If multiple landers are landing together, in order to avoid collisions and maintain good communication, appropriate distance should be maintained between the landers. In this paper, we first establish a landing environment model, and then propose a landing location planning method for a lander-group using genetic cooperative particle swarm optimization (GCPSO), which ensures the autonomous movement of individual landers while maintaining a certain safe landing distance between them. Taking a group of three landers as an example, simulation experiments show that the algorithm proposed in this paper can ensure the group to achieve stable landing and keep safe distances between them. © 2024 IEEE.

关键词： Landing

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DFE-SLAM: Dynamic SLAM based on improved feature extraction

DFE-SLAM: Dynamic SLAM based on improved feature extraction

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2024 China Automation Congress, CAC 2024

作者： Cheng, Guiying Jia, Jianfang Pang, Xiaoqiong Wen, Jie Shi, Yuanhao Zeng, Jianchao School of Electrical and Control Engineering North University of China Taiyuan China School of Computer Science and Technology North University of China Taiyuan China

ISBN: (纸本)9798350368604

Traditional visual localization algorithms often assume a static world, making them susceptible to inaccuracies and reduced robustness in real environments with dynamic objects. Additionally, these algorithms struggle to maintain high localization accuracy in low-texture settings. To overcome these challenges, we propose a dynamic SLAM system with enhanced feature extraction capabilities, called DFE-SLAM (Dynamic SLAM based on improved feature extraction). DFE-SLAM employs a lightweight YOLOv5s dynamic target detection network to filter out dynamic features from detection results. It also utilizes adaptive thresholding during the feature point detection stage to enhance accuracy. Moreover, the system incorporates a dense map construction thread, which creates dense point cloud maps using keyframes with corresponding bit poses. We evaluated DFE-SLAM using the TUM and Bonn datasets, benchmarking it against several advanced SLAM systems. The results demonstrate that DFE-SLAM excels in dynamic environments, delivering superior accuracy and robustness compared to most existing SLAM systems. © 2024 IEEE.

关键词： dense map Dynamic scenes feature extraction simultaneous localization and map building YOLOv5s

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Human-object interaction detection via interactive visual-semantic graph learning

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Science China(Information Sciences) 2022年第6期65卷 81-82页

作者： Tongtong WU Fuqing DUAN Liang CHANG Ke LU College of Artificial Intelligence Beijing Normal University School of Computer and Control Engineering University of Chinese Academy of Sciences

Dear editor,Human-object interaction(HOI) detection is an important human-centric visual understanding task with several applications in visual monitoring, intelligent robot, etc. It aims at localizing and inferring interaction relationships between humans and objects in images. As a result of their success in object detection, many object detectors can be used to localize human and object instances. Therefore, the key to HOI detection mainly lies in the second part, namely interaction recognition.

关键词： Human-object interaction Context modeling Graph Learning Interactive Graph Visual-Semantic Graph

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Disturbances Observer-Based Fixed-Time Sliding Mode control for Three-Level NPC Converters in Microgrid 22

Disturbances Observer-Based Fixed-Time Sliding Mode Control ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Liu, Guangxin Shen, Xiaoning Zhang, Jianhua Gao, Yabin Liu, Jianxing School of Astronautics Harbin Institute of Technology Harbin China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798331527471

This paper introduces a fixed-time sliding mode control (FTSMC) scheme that utilizes a fixed-time disturbance observer (FTDOB) for three-level neutral-point-clamped (3L-NPC) converters. Therein, a FTSMC with adaptive exponential coefficient is developed to regulate the dc-link voltage. Compared with existing methods, the proposed approach features a settling time upper bound that is independent of the initial error, which ensures the dynamic performance of the 3L-NPC converters. Furthermore, a FTDOB is employed to estimate external disturbances and act as feedforward compensation of the 3L-NPC converters to enhance its disturbances rejection ability. Finally, a series of simulation results illustrate the advantages of the proposed scheme by compared with several representative methods used in 3L-NPC converters. © 2024 IEEE.

关键词： Sliding mode control

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Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications 63

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Ag...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Engelaar, M.H.W. Zhang, Z. Vlahakis, E.E. Dimarogonas, D.V. Lazar, M. Haesaert, S. Netherlands Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden

ISBN: (纸本)9798350316339

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into subspecifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing. © 2024 IEEE.

关键词： Stochastic systems

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Multi-population Fruit Fly Optimization Algorithm with Genetic Operators for Multi-target Path Planning 1

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2nd International Conference on Cognitive Computation and Systems, ICCCS 2023

作者： Cheng, Ke Zhao, Qingjie Wang, Lei Liu, Wangwang Hu, Shichao Fang, Kairen School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

Automatic path planning is very important for many applications such as robots exploring unknown environments and logistics delivery. In this paper, we propose a discrete multi-population fruit fly optimization algorithm with genetic operators, where a greedy strategy is used to obtain good initial population, 3-opt heuristic search simulating olfactory to make the algorithm achieve higher convergence accuracy, multiple population collaborative strategy simulating vision to avoid the algorithm falling into local optima, and the genetic mechanism of selection-crossover-mutation prompts the population easily getting the optimal solution. The proposed planning algorithm has fewer parameters to be adjusted and has the advantages of high accuracy and fast convergence. The experimental results prove that the proposed planning algorithm performs best compared with other several algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Motion planning

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A GRU-based Automatic Weighting Factors Optimization Strategy for PMSM

A GRU-based Automatic Weighting Factors Optimization Strateg...

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2024 China Automation Congress, CAC 2024

作者： Li, Jiaao Jin, Jie Zhang, Jianhua Liu, Zhuang Shen, Xiaoning School of Astronautics Harbin Institute of Technology Harbin China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798350368604

The performance of model predictive control (MPC) in permanent magnet synchronous motor (PMSM) still remains a challenging problem due to the large torque ripple in lower speed. The cost function in conventional MPC generally use fixed weighting factors, which is not easy to adapt different operation scenario and keep balance between torque ripple suppression and current tracking accuracy. To address this issue, a gated recurrent units (GRU) network based weighting factors automated-regulation strategy is proposed for the MPC in PMSM. Through offline training, the weighting factors in MPC are tuned in real time and the current tracking accuracy as well as torque ripple in different conditions are ensured, which is superior to the conventional fixed weighting factors-based MPC. Finally, some simulation results verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Predictive control systems

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Wave refrigeration control via deep reinforcement learning 4

Wave refrigeration control via deep reinforcement learning

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2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2024

作者： Cui, Xuesong Hu, Xiaopeng Wang, Fan Li, Qi School of Computer Science and Technology Dalian University of Technology Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

Wave refrigerating technology is a new type of refrigerating technology that performs more efficiently and is more environmentally friendly. Nowadays, the commonly used control method is the PID control method. However, although the above control methods are simple in structure, they have the problems of difficult parameter tuning, large overshoot, and unstable and inaccurate output response. Therefore, to improve the above problems, we propose a framework of deep reinforcement learning-based wave refrigerating control system. An improved SAC algorithm is used to learn the control system. The evaluation method is established according to the working condition requirements as well as the safety requirements in the operation process. Experiments show that this method can effectively improve the control efficiency as well as the control quality, which can make the control system operate stably. © Published under licence by IOP Publishing Ltd.

关键词： Deep reinforcement learning

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Reinforcement learning-based unknown reference tracking control of HMASs with nonidentical communication delays

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Science China(Information Sciences) 2023年第7期66卷 46-57页

作者： Yong XU Zheng-Guang WU Wei-Wei CHE Deyuan MENG School of Automation Beijing Institute of Technology Institute of Cyber-Systems and Control Zhejiang University College of Mathematics and Computer Science Zhejiang Normal University Department of Automation Qingdao University School of Automation Science and Electrical Engineering Beihang University(BUAA)

This paper focuses on the optimal output synchronization control problem of heterogeneous multiagent systems(HMASs) subject to nonidentical communication delays by a reinforcement learning *** with existing studies assuming that the precise model of the leader is globally or distributively accessible to all or some of the followers, the leader's precise dynamical model is entirely inaccessible to all the followers in this paper. A data-based learning algorithm is first proposed to reconstruct the leader's unknown system matrix online. A distributed predictor subject to communication delays is further devised to estimate the leader's state, where interaction delays are allowed to be nonidentical. Then, a learning-based local controller, together with a discounted performance function, is projected to reach the optimal output synchronization. Bellman equations and game algebraic Riccati equations are constructed to learn the optimal solution by developing a model-based reinforcement learning(RL) algorithm online without solving regulator equations, which is followed by a model-free off-policy RL algorithm to relax the requirement of all agents' dynamics faced by the model-based RL algorithm. The optimal tracking control of HMASs subject to unknown leader dynamics and communication delays is shown to be solvable under the proposed RL algorithms. Finally, the effectiveness of theoretical analysis is verified by numerical simulations.

关键词： heterogeneous multiagent systems HMAS reinforcement learning RL optimal output synchronization communication delays

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Investigating the impacts of spatial-temporal variation features of air density on assessing wind power generation and its fluctuation in China

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Science China(Technological Sciences) 2023年第6期66卷 1797-1814页

作者： REN GuoRui WANG Wei WAN Jie HONG Feng YANG Ke YU DaRen School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China Xi’an Thermal Power Research Institute Co. Ltd.Xi’an 710054China School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China

Air density plays an important role in assessing wind *** density significantly fluctuates both spatially and *** literature typically used standard air density or local annual average air density to assess wind *** present study investigates the estimation errors of the potential and fluctuation of wind resource caused by neglecting the spatial-temporal variation features of air density in *** air density at 100 m height is accurately calculated by using air temperature,pressure,and *** spatial-temporal variation features of air density are firstly *** the wind power generation is modeled based on a 1.5 MW wind turbine model by using the actual air density,standard air densityρst,and local annual average air densityρsite,***ρstoverestimates the annual wind energy production(AEP)in 93.6%of the study *** significantly affects AEP in central and southern China *** more than 75%of the study area,the winter to summer differences in AEP are underestimated,but the intra-day peak-valley differences and fluctuation rate of wind power are ***ρsitesignificantly reduces the estimation error in *** AEP is still overestimated(0-8.6%)in summer and underestimated(0-11.2%)in *** for southwest China,it is hard to reduce the estimation errors of winter to summer differences in AEP by usingρ***ρsitedistinctly reduces the estimation errors of intra-day peak-valley differences and fluctuation rate of wind power,but these estimation errors cannot be ignored as *** impacts of air density on assessing wind resource are almost independent of the wind turbine types.

关键词： air density spatial-temporal variation power curve wind resource assessment fluctuation ERA-5

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