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Six-Degree-of-Freedom Intelligent control of Hypersonic Flight Vehicle

IEEE Transactions on Aerospace and Electronic systems 2025年

作者： An, Hao Wang, Yiming Zhong, Xinyu Ye, Yongfeng Harbin Institute of Technology Space Control and Inertial Technology Research Center National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Harbin China

The six-degree-of-freedom control of hypersonic flight vehicle (HFV) faces great challenges due to complex aerodynamics, inherent couplings and multiple constraints. This paper investigates the six-degree-of-freedom guidance and attitude control problem for unpowered gliding HFV. The guidance loop is based on the predictor-corrector method with three improvements of intelligent prediction acceleration, path constraints accommodation and no-fly zones circumvention. To quickly respond to guidance commands, three attitude channels (i.e., roll, pitch and yaw) are completely decoupled by nonlinear dynamic inverse, based on which a high-performance low-complexity attitude controller is proposed. This benefits from the appropriate integration of barrier function and sliding mode control, while design conditions of three alternative performances (i.e., asymptotic, exponential and preset-time convergences) are discussed. Servo dynamics and physical constraints of aerodynamic control surfaces are also well handled. © 2025 IEEE.

关键词： Hypersonic vehicles

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Dual and reduced-order observer based attack-free protocols for consensus of multi-agent systems with Markovian switching topologies

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systems and control Letters 2025年 202卷

作者： Gao, Tian-Yu Li, Zhao-Yan Zhou, Bin Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin150001 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Harbin150001 China School of Mathematics Harbin Institute of Technology Harbin150001 China Zhengzhou Research Institute of HIT Zhengzhou450000 China

This paper studies the output feedback consensus problem of linear multi-agent systems with Markovian switching topologies. To avoid reconstructing the Markovian switching topologies during designing the observers or adding the state of the observers to the information exchange loop, dual and reduced-order observer based attack-free consensus protocols are proposed, which only utilizes the relative outputs of neighboring agents. It is shown that, under certain mild conditions, a necessary and sufficient condition for the solvability of the mean square consensus problem is that the union of switching topologies has a spanning tree. Finally, numerical examples are given to illustrate the effectiveness of the proposed approaches. © 2025 Elsevier B.V.

关键词： Markov processes

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A MPC-based Planner Applied on a Parallel Wheel-legged Robot for Obstacle Avoidance 25

A MPC-based Planner Applied on a Parallel Wheel-legged Robot...

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and control Engineering

作者： Fei Guo Huan Yu Wanhong Lin Shoukun Wang National Key Laboratory of Human Factors Engineering China Astronaut Research and Training Center Beijing China Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China

ISBN: (纸本)9798400712647

A wheel-legged robot is equipped with Stewart parallel mechanism, constituting a reconfigurable robot which can change its wheelbase, robot body height, and achieve omnidirectional steering. The legged character effectively improves the terrain adaptability, which concerns our planning concentration. We introduced an optimization-based whole-body trajectory planning algorithm to navigate robot in rugged terrain. The planner combines terrain data and stability, allowing lower-level motion generator and controller to operate more efficiently. The Model Predictive control(MPC)-based method updates the footholds and CoG trajectories, which builds upon the support polygon constraints on optimization. The simulations of methodology working in several structure-obstacle scene demonstrated and compared the availability of approach.

关键词： Reconfigurable robot

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Adsorption of monolayer penta-CdSe2 by atoms: A first-principles study

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Journal of Physics and Chemistry of Solids 2025年 206卷

作者： Wei, Xiao-Ping Meng, Jiang-Liu Sun, Hao-Kai Zhang, Ya-Ling Tao, Xiaoma The School of Mathematics and Physics Lanzhou Jiaotong University Lanzhou730070 China Gansu Center for Fundamental Research in Complex Systems Analysis and Control Lanzhou Jiaotong University Gansu Lanzhou730070 China Institute of Modern Physics Chinese Academy of Sciences Lanzhou730000 China College of Physical Science and Technology Guangxi University Nanning530004 China

Pentagonal CdSe2 is a theoretically predicted two-dimensional (2D) material with room-temperature stability, offers unique electronic and structural properties but remains unexplored in terms of adsorption-driven functionalization. Using first-principles density-functional theory calculations, we systematically investigate the adsorption of 14 atoms, including non-metals (H, C, N, O, F), alkali and alkaline-earth metals (Li, Na, K, Mg, Ca), and noble metals (Pd, Ag, Pt, Au)—on monolayer CdSe2. All adsorption processes are energetically favorable, with adsorption energies spanning −0.39 to −4.86 eV, where noble metals (e.g., Pt: 4.83 eV) and non-metals (e.g., C: 4.86 eV) exhibit the strongest binding. H, N, Li, Na, Ag, and Au adsorption induces ferromagnetism, with magnetic moments ranging 0.56–0.98 μB, transforming non-magnetic CdSe2 into a magnetic semiconductor or metal. F and K adsorption metallizes the system by reducing the bandgap from 1.48 eV (pristine) to 0.00 eV. Work function is tunable over 4.49–5.89 eV, with alkali metals significantly lowering work function, ideal for photoelectron emission. Charge transfer analyses reveal ionic bonding for F (−0.75 e) and covalent/metallic interactions for C, N, and O. These results demonstrate that targeted atomic adsorption enables precise control over CdSe2's electronic, magnetic, and surface properties, positioning it for applications in spintronics, catalysis, and energy storage. Our work bridges theoretical predictions with functional design, urging experimental validation of CdSe2 synthesis and adsorption engineering. © 2025 Elsevier Ltd

关键词： Local density approximation

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Crowd evacuation simulation in multi-agent based virtual environment

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2012 International Conference on Cybernetics and Informatics, ICCI 2012

作者： Cai, Linqin Zhang, Zhenhua Pan, Yuyou Wang, Na Research Center on Complex System Analysis and Control Chongqing University of Posts and Telecommunications Chongqing 400065 China

ISBN: (纸本)9781461438717

To explore crowd behaviors in coalmine emergency situations, a crowd evacuation simulation model was presented, which aims at the high-level decision-making skills and wayfinding algorithm to explore the leadership in underground emergency evacuation. In proposed evacuation model, leaders' and followers' behaviors were constructed according to their different roles and internal mental states. Leaders can explore coalmine environment for new paths using the proposed wayfinding algorithm to help others. The simulating results of underground fire evacuation case show the proposed approach is effective to create believable crowd behaviors in virtual coalmine. © 2013 Springer Science+Business Media.

关键词： Virtual reality

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Development of a neural network model for forecasting energy consumption in energy management systems

Development of a neural network model for forecasting energy...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Valeriya Tynchenko Ilia Panfilov Ksenia Degtyareva Natalia Kandaurova Tatyana Dolgova Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow Russia Software Research and Training Laboratory Siberaim Federal Iniversity Krasnoyarsk Russia Information-Control Systems Department Reshetnev Siberian State University of Science and Technology Department of Systems Analysis and Operations Research Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Center for Continuing Education Bauman Moscow State Technical University Moscow Russia Department of Information Economic System Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper explores the use of neural networks to predict energy consumption based on data on the production and use of various fuels, including oil, gas and coal. The aim of the study is to develop a model that can take into account the complex relationships between key parameters such as production volumes, production costs, CO₂ emissions, energy efficiency, inventory levels and transportation costs. The relevance of the study is due to the need to improve the accuracy of forecasting for effective planning and management of resources in the energy industry, especially in conditions of unstable market and environmental factors. In the course of the work, a multi-layered fully connected neural network model was developed, which was trained and tested on the prepared data. The evaluation of the model using the coefficient of determination and the average absolute error demonstrated its high accuracy and suitability for practical application. Additionally, an analysis of the importance of the signs was carried out, which showed that the most significant factors are the import/export of energy and the share of alternative sources. Visualization of the results confirmed that the model is able to make accurate predictions, minimizing errors. The results obtained confirm that the use of neural network models to predict energy consumption is a promising area that can significantly improve decision-making processes in the energy sector and increase resource efficiency.

关键词： Industries Energy consumption Analytical models Accuracy Costs Neural networks Production Predictive models Data models Forecasting

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Changes in snowmelt flooding and driver factors in the China-Pakistan Economic Corridor

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Journal of Hydrology 2025年 660卷

作者： Zhang, Haiting Xu, Min Kang, Shichang Li, Xingdong School of Mathematics and Physics Lanzhou Jiaotong University Lanzhou730070 China State Key Laboratory of Cryospheric Science and Frozen Soil Engineering Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou730000 China China-Pakistan Joint Research Center on Earth Sciences Chinese Academy of Sciences Chengdu 610299 China University of Chinese Academy of Sciences Beijing 100049 China Gansu Center for Fundamental Research in Complex Systems Analysis and Control Lanzhou Jiaotong University Lanzhou Gansu 730070 China

Flooding is one of the most frequent disasters in the China-Pakistan Economic Corridor (CPEC), which is highly dangerous and destructive, and seriously hinders regional socio-economic development. In this study, the Block Maximum (BM) model and the Peak Over Threshold (POT) model were used to analyze the spatial and temporal characteristics of snowmelt floods in the Kashgar River Basin and the Indus River Basin of CPEC during 1948–2022. Historical floods were classified based on principal component analysis and the k-means clustering algorithm. The main climatic factors of snowmelt floods were analyzed using Spearman's rank correlation coefficient and random forest model. The results indicated that 85 and 93 snowmelt flood events occurred respectively in the two basins, mainly in spring (March–May). The occurrence of snowmelt floods was advanced in the Kashgar River Basin, while that in the Indus River Basin was delayed. Five and four flood classes were identified in the Kashgar River Basin and the Indus River Basin, respectively. Attribution analysis suggested that the snow factors are key preconditions, especially the 14-day antecedent maximum SWE (MSWE). The Kashgar River Basin was more sensitive to 7-day cumulative precipitation (CPr7) from 1982 to 1999, but to 7-day cumulative daily average temperature (TEMP7) from 2000 to 2020. The Indus River Basin was more sensitive to 1-day cumulative precipitation (CPr1) from 1982 to 1999. However, the impact of CPr7 on snowmelt floods increased from 2000 to 2020. The study provides scientific and technological support for snowmelt flood forecasting and prevention in CPEC and regional disaster prevention and mitigation efforts. © 2025 Elsevier B.V.

关键词： Global warming

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Development of a neural network model for forecasting energy consumption in energy management systems 24

Development of a neural network model for forecasting energy...

引用

24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Tynchenko, Valeriya Panfilov, Ilia Degtyareva, Ksenia Kandaurova, Natalia Dolgova, Tatyana Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow Russia Department of Systems Analysis and Operations Research Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Center for Continuing Education Bauman Moscow State Technical University Moscow Russia Department of Information Economic System Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Software Research and Training Laboratory Siberaim Federal Iniversity Krasnoyarsk Russia Information-Control Systems Department Reshetnev Siberian State University of Science and Technology Russia

ISBN: (纸本)9798331515799

This paper explores the use of neural networks to predict energy consumption based on data on the production and use of various fuels, including oil, gas and coal. The aim of the study is to develop a model that can take into account the complex relationships between key parameters such as production volumes, production costs, CO emissions, energy efficiency, inventory levels and transportation costs. The relevance of the study is due to the need to improve the accuracy of forecasting for effective planning and management of resources in the energy industry, especially in conditions of unstable market and environmental factors. In the course of the work, a multi-layered fully connected neural network model was developed, which was trained and tested on the prepared data. The evaluation of the model using the coefficient of determination and the average absolute error demonstrated its high accuracy and suitability for practical application. Additionally, an analysis of the importance of the signs was carried out, which showed that the most significant factors are the import/export of energy and the share of alternative sources. Visualization of the results confirmed that the model is able to make accurate predictions, minimizing errors. The results obtained confirm that the use of neural network models to predict energy consumption is a promising area that can significantly improve decision-making processes in the energy sector and increase resource efficiency. © 2025 IEEE.

关键词： Multilayer neural networks

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Condition Monitoring for Railway Point Machines Based on Sound analysis and Support Vector Machine

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Chinese Journal of Electronics 2020年第4期29卷 786-792页

作者： SUN Yongkui CAO Yuan XIE Guo WEN Tao School of Electronic and Information Engineering Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Railway point machines(RPMS) are one of the key equipments in the railway system to switch different routes for the *** monitoring for RPMs is a vital measure to keep train operation safe and *** convenience and low cost into consideration, a novel intelligent condition monitoring method for RPMs based on sound analysis is ***-domain and frequency-domain features are obtained,and normalized using z-score standardization method to eliminate the influences of different *** particle swarm optimization(BPSO) is utilized to select the most significant discrimination feature *** effects of the selected optimal features are verified using Support vector machine(SVM), 1-Nearest neighbor(1 NN), Random forest(RF), and Naive Bayes(NB).Experiment results indicate SVM performs best on identification accuracy and computing cost compared with the other three *** identification accuracies on normal switching and reverse switching processes reach100% and 99.67%, respectively, indicating the feasibility of the proposed method.

关键词： Condition monitoring Sound analysis Railway point machines(RPMs) Binary particle swarm optimization(BPSO) Support vector machine(SVM)

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Intelligent model for assessing the suitability of water based on its chemical and biological parameters

Intelligent model for assessing the suitability of water bas...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Yadviga Tynchenko Vadim Tynchenko Ksenia Degtyareva Georgy Kuznetsov Svetlana Kukartseva Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk Russia Information-Control Systems Department Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Department of Applied Informatics Moscow Timiryazev Agricultural Academy Moscow Russia Department of Systems Analysis and Operations Research Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Center for Continuing Education Bauman Moscow State Technical University Moscow Russia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper presents a study on the development and testing of a machine learning model for classifying water according to its purpose, based on chemical and microbiological indicators. The aim of the study is to create an accurate and stable model that can effectively determine the categories of water — drinking, industrial, agricultural and technical — based on the analysis of its qualitative characteristics. The gradient boosting method was chosen as the algorithm for classification, which demonstrated high accuracy and reliability when working with multidimensional data. The study included the processing of a synthetic dataset containing information on water parameters such as calcium concentration, total bacterial content, phosphate levels, alkalinity, sulfates and oxidizability. The analysis of the correlation matrix made it possible to identify the relationships between the features, which made it possible to optimize the operation of the model and improve its accuracy. The quality of the model was tested using accuracy, completeness and F1-score metrics, which demonstrated almost perfect results for all categories. In conclusion, it is concluded that the proposed model can be successfully used to automate the analysis of water quality in ecology, industry and agriculture. The use of intelligent technologies opens up new opportunities for monitoring water resources, increasing the efficiency and reliability of quality control.

关键词： Measurement Analytical models Accuracy Correlation Biological system modeling Water quality Boosting Reliability Water resources Chemicals

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