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Multi-Targets Visiting Orbit Design Based on Numerical Method

Guidance,Navigation and Control 2025年第1期05卷 139-148页

作者： Zenan Zhong Zhengxiang Sun Songyan Wang Tao Chao Ming Yang Control and Simulation Center Harbin Institute of Technology National Key Laboratory of Modeling and Simulation for Complex Systems Harbin Institute of Technology Beijing Institute of Space Long March Vehicle

In this paper, for the two/three targets in the circle orbit with the same altitude, a numerical method is presented to design an orbit for a spacecraft to visit multiple targets without maneuvering. First,for the two targets' case, an equation is established by the relationship of the two angles that both are the function of the time, one of the angles is the angle between two targets' geocentric position vectors at two times, and another one is the spacecraft flight angle during the two times. The visit time can be solved by the assumption that one of the visit times is known in the equation, and the orbit element can be transferred from the position and velocity vector determined by the solved visit time. Then, for the three targets' case, the problem is decoupled into two cases of two targets, and there is a public target in each case. Based on the method for two targets, two different orbits can be obtained by determining the visit time of the public target. The difference between two orbits can be expressed by the difference in velocity vectors of the public target at different times. A sampling method is utilized to select two velocity vectors with relatively small differences as initial values, the Newton iteration method is used to obtain visit times with higher accuracy. simulation results illustrate the effectiveness of the orbit design methods.

关键词： Orbit design method multiple targets visit circle orbit non-maneuvering

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Control Design of Flight simulation Turntable Based on Model-Free Adaptive Control

Control Design of Flight Simulation Turntable Based on Model...

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2023 China Automation Congress, CAC 2023

作者： Yue, Mingtan Zhang, Yu Huo, Xin Control & Simulation Center Harbin Institute of Technology Harbin150080 China Beijing Simulation Center National Key Laboratory of Modeling and Simulation for Complex Systems Beijing100854 China

ISBN: (纸本)9798350303759

Aiming at the problem of periodic interference in flight simulation turntables, a design method of full-form dynamic linearization model free adaptive control (FFDL-MFAC) is proposed. An equivalent dynamic linearized data model is established at each dynamic operating point of the closed-loop system to achieve adaptive control of the nonlinear system, which gives the controller the advantage of online adjustment of internal parameters and improves the robustness of the control system. Aiming at the difficulty of parameter adjustment caused by the increase of dimension of the pseudo partial derivative matrix, a PID-like MFAC parameter adjustment method is proposed, which greatly reduces the difficulty of parameter adjustment of the model-free adaptive controller. The simulation results of MFAC and PID show that the designed FFDL-MFAC controller outperforms the conventional PID in tracking command signals and suppressing periodic disturbances. © 2023 IEEE.

关键词： Controllers

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DMGA: A Directional-Mutation Genetic Algorithm for CDMS Problem of Kafka 21

DMGA: A Directional-Mutation Genetic Algorithm for CDMS Prob...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Ma, Hanbo Ma, Yaofei Yuan, Haitao Wang, Meijia Hu, Jingwei Wang, Yihuan Yang, Hanbo School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Simulation Center National Key Laboratory of Modeling and Simulation for Complex Systems Beijing China

ISBN: (纸本)9798350365221

With the increasing role of distributed message systems (DMS) represented by Kafka in handling large-scale data streams, it is critical to improve system performance by optimizing configuration parameters in different production environments. Due to the target system possessing numerous configuration parameters and the complex interactions between them, parameter optimization becomes a complex and time-consuming process that relies heavily on human effort and an iterative trial-and-error approach. At present, this type of configuration parameter problem has been classified as the Configuration of Distributed Message systems (CDMS) problem, for which the goal is to obtain the optimal set of parameters for achieving the best performance. In order to deal with this complex problem, an automatic optimization method is proposed in this paper, which combines an automatic script-based deployment and running framework with an improved Genetic Algorithm (GA) in cooperation with the Random Forest (RF) and statistical methods, named the Directional-Mutation Genetic Algorithm (DMGA). The framework and algorithm are designed to find the best configuration efficiently, obtaining optimal parameters accurately without human supervision. Actual results of comparison experiments based on the Kafka system show that the DMGA algorithm significantly improves the data throughput performance of Kafka, and its optimization speed is more efficient than the basic GA, highlighting its potential to quickly optimize Kafka's operational efficiency in different scenarios. © 2024 IEEE.

关键词： Genetic algorithms

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Autonomous Decision Making with Reinforcement Learning in Multi-UAV Air Combat

Autonomous Decision Making with Reinforcement Learning in Mu...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Feng, Xutao Ma, Yaofei Zhao, Liping Yang, Hanbo Beihang University School of Automation Science and Electrical Engineering Beijing China Beijing Simulation Center National Key Laboratory of Modeling and Simulation for Complex Systems Beijing China

ISBN: (纸本)9798350337020

A multi-agent decision network based on QMIX is proposed in this paper to cope with the coordination decision problem of multiple UAV air combat missions. To speed up the training process, three improvements are introduced: 1) An improved ϵ-decaying method that enable some tutor to help in action selection at the early stage of the training. This measure greatly improves the exploring efficiency when the network are far from being fully trained;2) State pruning and action mask measures are applied during the training. The former improves the effectiveness of the input state information, and the latter reduces unnecessary action exploring. 3) A gradually training configuration is used to make the training process more robust, where the combat adversaries are configured as the static targets, the randomly maneuver vehicles, and the Min-Max strategy vehicles respectively. The multi-UAV air combat scenarios are built up and the experiments are conducted. The results shows that these improvements have significantly improved training efficiency. © 2023 IEEE.

关键词： Action Mask Air combat MARL Min-Max strategy Quadrotor UAV

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Optimal Deployment of Target Localization With Distance-Dependent Noise

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IEEE Internet of Things Journal 2025年第12期12卷 20959-20972页

作者： Tian, Junxi Yang, Ming Chao, Tao Harbin Institute of Technology Control and Simulation Center National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150001 China

The cooperative localization of radio-based distance measurement is one of the methods to effectively suppress the nonline of sight (NLOS) ranging errors and as a complement to visual/global navigation satellite system (GNSS) localization for unmanned aerial vehicle (UAV) in GNSS challenging or denied environments due to its motility. Target localization can be regarded as a special case of cooperative localization. Meanwhile, the UAV can be considered as a ranging node when the ranging terminal is rigidly mounted on the UAV. To improve the target localization accuracy, the optimal deployment of anchor UAVs is one of the factors we consider when the noise characteristics are different. In this paper, the range-dependent noise in a real flight environment is considered and modeled separately according to the difference in noise characteristics. Subsequently, based on this correlation model, the Fisher information matrix (FIM) and a cost function derived from the A-optimal criterion are provided. Finally, the optimal deployment conditions and the minimum mean square error (MMSE) can be derived by minimizing the cost function as the objective. Through simulation and localization experiments, the validity of the optimal deployment conditions and the corresponding MMSE are verified. Meanwhile, it is concluded that when the noise is range-dependent, it will not affect the optimal deployment result but will significantly change the value of MMSE. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Autonomous Vehicle Path Planning Algorithm Based on Improved Q-learning

Autonomous Vehicle Path Planning Algorithm Based on Improved...

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International Conference on Guidance, Navigation and Control, ICGNC 2024

作者： Wang, Yuelong Wang, Songyan Chao, Tao Control and Simulation Center Harbin Institute of Technology Harbin 150080 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin Institute of Technology Harbin 150001 China

ISBN: (纸本)9789819622115

In the context of the rapid evolution of autonomous driving technology, the deployment of autonomous vehicle systems has witnessed significant expansion, underscoring the paramount importance of their path planning capabilities. This study proposes an improved Q-learning, specifically designed for the path planning of autonomous vehicles. Initially, the algorithm is enhanced with an advanced artificial potential field method for initializing the Q-table, thereby integrating prior knowledge and increasing the efficiency of the initial iterations. Further, the integration of eligibility traces as a memory mechanism within the algorithm ensures the prolonged influence of state-action pairs throughout the iterative process, thus boosting learning efficiency. Additionally, this paper introduces a dynamic λ adjustment strategy, tailored to meet the exploratory demands of autonomous vehicles and based on the simulated annealing algorithm. This strategy dynamically modulates the decay factor λ in accordance with the frequency of successful arrivals at the destinations, promoting more effective adaptation to varying environmental conditions. The empirical findings confirm the efficacy of the proposed Q-learning, particularly highlighting its utility in the path planning for autonomous vehicles. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autonomous Vehicle Path Planning Q-learning

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A credibility allocation approach of complex simulation systems 35

A credibility allocation approach of complex simulation syst...

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35th European modeling and simulation Symposium, EMSS 2023

作者： Zhang, Huan Li, Wei Geng, HuaPin Ma, Ping Yang, Ming Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150080 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9788885741881

Complex simulation systems usually need to satisfy the credibility requirements and the credibility of complex simulation systems is inextricably linked to the credibility of simulation sub-systems. Hence, in order to guarantee the credibility of complex simulation systems meets requirements, it is necessary to allocate the credibility of simulation sub-systems. Complex simulation systems are composed of several correlative simulation sub-systems and their credibility has correlation relationships, which makes it difficult to obtain the credibility of simulation sub-systems quickly. The existing works mostly ignore the relationships between the sub-systems and are difficult to generate the credibility allocation results of simulation sub-systems. In this paper, we model the credibility allocation problem as a probabilistic inference problem based on the pairwise Markov random field (PMRF). In addition, we apply several inference algorithms to perform the inference exactly and approximately in the credibility PMRF. Finally, experiments are conduted on the air defense combat complex simulation system, and the experiment results demonstrate that the proposed method can obtain the credibility allocation results quickly and accurately. © 2023 The Authors.

关键词： Inference engines

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Impact Angle Control Guidance Law Against Maneuvering Targets

Impact Angle Control Guidance Law Against Maneuvering Target...

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International Conference on Guidance, Navigation and Control, ICGNC 2024

作者： Du, He Yang, Ming Wang, Songyan Chao, Tao National Key Laboratory of Modeling and Simulation for Complex Systems Harbin Institute of Technology Heilongjiang150001 China Control and Simulation Center Harbin Institute of Technology Heilongjiang150001 China

ISBN: (纸本)9789819622313

To delineate a planar engagement scenario through mathematical expressions, the paper first establishes the connection between the desired line-of-sight and flight-path angle. Unlike conventional guidance laws that assume zero target maneuvering, which often results in significant impact angle errors, this approach employs an extended state observer to enhance tracking performance. Utilizing the error of angle, a sliding mode is devised to design the guidance law, with the stability of the proposed method validated through Lyapunov theory. The effectiveness and robustness of the method are demonstrated through numerical scenarios. This method offers precise estimation of target maneuvering and facilitates interception. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sliding mode control

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DMGA: A Directional-Mutation Genetic Algorithm for CDMS Problem of Kafka

DMGA: A Directional-Mutation Genetic Algorithm for CDMS Prob...

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IEEE International Conference on Networking, Sensing and Control

作者： Hanbo Ma Yaofei Ma Haitao Yuan Meijia Wang Jingwei Hu Yihuan Wang Hanbo Yang School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Simulation Center National Key Laboratory of Modeling and Simulation for Complex Systems Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

关键词： Statistical analysis System performance Production Throughput Sensors Tuning Streams Genetic algorithms Message systems Convergence

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Federated Learning With Client Clustering Selection and Quality-Aware Model Aggregation(2024)

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IEEE Internet of Things Journal 2025年

作者： Peng, Y. Wang, C. Shi, H. Ma, R. Guan, H. Yang, Hanbo Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Beijing Simulation Center National Key Laboratory of Modeling and Simulation for Complex Systems Beijing China

The Industrial Internet of Things (IIoT) is revolutionizing industries through device interconnectivity, enabling real-time data collection and transmission for enhanced monitoring, control, and automation. This has led to improvements in predictive maintenance, production optimization, and supply chain management. The integration of machine learning (ML) and deep learning (DL) within IIoT has been accelerated by increased data availability, computational advancements, and algorithmic innovations, with applications ranging from image processing to autonomous vehicles. Foundation models, such as ChatGPT, are becoming prevalent in IIoT for their capabilities in natural language processing and computer vision. However, the growth of IIoT and foundation models presents challenges, including data volume, real-time processing requirements, computational costs, and security vulnerabilities. Federated learning (FL) addresses these issues by allowing distributed model training without raw data transfer, enhancing privacy and security. FL is particularly beneficial for IIoT’s decentralized architecture and real-time decision-making needs. Despite the advantages, FL faces challenges such as data heterogeneity and communication overhead. To overcome these, we propose a federated learning framework, Fed-CCSQMA, which includes client selection and model aggregation modules to mitigate data heterogeneity’s impact. The client selection module uses Principal Component Analysis (PCA) and clustering to select clients with diverse yet representative data, while the model aggregation module assigns weights based on model accuracy to ensure faster global model convergence. We test our proposed framework on FMNIST and CIFAR-10 datasets, with accuracy surpassing baseline methods and faster convergence, demonstrating an improvement in overall generalizability and learning efficiency. © 2014 IEEE.

关键词： Client Selection Clustering Federated Learning Model Aggregation

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