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A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

Journal of Systems engineering and Electronics 2024年第3期35卷 732-740页

作者： LYU Xu MENG Ziyang LI Chunyu CAI Zhenyu HUANG Yi LI Xiaoyong YU Xingkai Department of Precision Instrument Tsinghua UniversityBeijing 100084China College of Mechanical and Power Engineering Three Gorges UniversityYichang 443002China Unit 91001 of the PLA Beijing 100161China School of Control and Computer Engineering North China Electric Power UniversityBeijing 100096China

In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF)master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

关键词： Kalman filter dual-adaptive integrated navigation unscented Kalman filter(UKF) robust

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An Improved Hybrid Aquila Optimizer and Pigeon-Inspired Optimization Algorithm with Its Application in PID Parameter Optimization 8

An Improved Hybrid Aquila Optimizer and Pigeon-Inspired Opti...

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8th International Symposium on computer Science and Intelligent control, ISCSIC 2024

作者： Chen, Dongning Du, Xinwei Wang, Haowen Xian, Qinggui Sha, Jianhao Yao, Chengyu Yanshan University School of Mechanical Engineering Qinhuangdao China Yanshan University Hebei Key Laboratory of Industrial Computer Control Engineering Qinhuangdao China

ISBN: (纸本)9798350380286

To improve the optimization performance of the standard Aquila Optimizer (AO), an Improved Hybrid Aquila Optimizer and Pigeon-Inspired Optimization (IHAOPIO) algorithm based on stochastic balancing factor and Dimension-by-dimension Centroid Opposition-Based Learning (DCOBL) strategy is proposed. Wilcoxon test is introduced to statistically analyze the data of six classical benchmark functions. Compared with several other optimization algorithms, the proposed IHAOPIO algorithm has better optimization capability in accuracy. The improved algorithm is used to the PID parameter optimization of one electro-hydraulic position servo system and achieves good control effect. © 2024 IEEE.

关键词： Optimization algorithms

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Landing location planning based on genetic cooperative particle swarm optimization 7

Landing location planning based on genetic cooperative parti...

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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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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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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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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multi-physical analysis and motor design optimization. This paper proposes a new method for thermal steady state modeling and analysis based on transfer learning, which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on transfer learning is developed: the model is pre-trained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R2 and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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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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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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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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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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