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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Pengfei Yang Zhaohui Yuan Sheng Dong Jingchao Li School of Automation Northwestern Polytechnical University Xi'an Shaanxi PR China School of Electrical and control engineering Shaanxi University of Science and Technology Xi'an Shaanxi PR China

To enhance the anti-disturbance ability of biped robots and improve their full range of motion ability, this paper proposes a gait generation method based on model predictive control with turning ability, which can automatically generate gait trajectories with the given reference speed and turning speed. We integrate footstep positions into the objective function and use the change of center of pressure (CoP) as the optimal input to minimize the objective. The contribution of this paper is considering various feasible optimization constraints to generate gait trajectories online with anti-disturbance ability, adjustable stride, and turning ability. Simulation results show that this method can generate robust gait trajectories online and turn in any direction.

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Path Optimization Study of Military Material Based on Time Uncertainty

Path Optimization Study of Military Material Based on Time U...

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Artificial Intelligence and automation control (AIAC), International Conference on

作者： Hua Gong Shiqi Li Yiying Shi School of Science Shenyang Ligong University Shenyang China Liaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry Shenyang China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

Aiming at the path optimization problem of military material based on time uncertainty, a multi-objective distribution model with a fuzzy time window was established with the shortest distribution path and the smallest time disturbance measure as the objective function. Combined weight method was used to assign weighted values to all kinds of military materials needed by combat points. The material urgency of the combat point is obtained, and then the time disturbance measurement is constructed. A genetic algorithm is used to solve the multiobjective optimization problem.

关键词： Uncertainty Heuristic algorithms Perturbation methods Optimization models Measurement uncertainty Linear programming Time measurement Vehicle dynamics Optimization Genetic algorithms

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Data-based Stabilization of Unknown Discrete-time 2-D Roesser Systems

Data-based Stabilization of Unknown Discrete-time 2-D Roesse...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Runmin Yang Rongni Yang Renjie Ma Yanzheng Zhu Peng Shi School of Control Science and Engineering Shandong University Jinan China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China School of Electrical and Mechanical Engineering University of Adelaide Adelaide SA Australia

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

This paper studies the stabilization problem of discrete-time two-dimensional (2-D) systems represented by Roesser based on available data. First of all, based on the pre-collected input-state data, the original system is transformed into a data-based form. Then a set is established to represent all systems capable of generating the given data, thereby indirectly stabilizing the original system by stabilizing all systems within this set. Next, by utilizing the S-procedure, sufficient conditions are established for the resultant closed-loop systems that incorporate a state feedback controller based on collected data. Finally, an example is given to demonstrate the effectiveness of the proposed approach.

关键词： Sufficient conditions State feedback Two-dimensional displays Transforms Machine learning Stability analysis Data models Numerical models Closed loop systems Numerical stability

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Multi-feature Fusion Based Deep Forest for Hyperspectral Image Classification 16th

Multi-feature Fusion Based Deep Forest for Hyperspectral Ima...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Liu, Xiaobo Mohammad, Mostofa Zaman Zhang, Chaochao Gong, Xin Cai, Zhihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Computer Science China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789811912528

Multi-feature fusion is a useful way to improve the classification of hyperspectral image (HSI). But the multi-feature fusion is usually at the decision level of classifier, which causes less link between features or poor extensibility of feature. In this paper, we propose a multi-feature fusion based deep forest method for HSI classification, named mfdForest. In mfdForest, the morphological features, saliency features, and edge features are extracted, then the three deep multi-grained scanning branches in dgcForest (one of improved deep forest) are used to extract and fuse the extracted features deeply, and the fused features are sent into cascade forest in dgcForest for classification. Experimental results indicate that the proposed framework consumes less training time and has better performance on two HSI data sets. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Spectroscopy

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Omnidirectional Vector Measurement Method for Accurate Laser Positioning System

Omnidirectional Vector Measurement Method for Accurate Laser...

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IEEE International Conference of Intelligent Robotic and control engineering (IRCE)

作者： Xiao Pan Fei Liu Qing Liu Zhen Wang Jiapei Li Pengfei Cheng Xi'an Aerocomm Measurement and Control Technology Co. Ltd. Xi'an China School of Materials Science and Engineering Xi'an University of Science and Technology Xi'an China School of Automation and Information Engineering Xi'an University of Technology Xi'an China School of Mechanical and Precision Instrument Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

The accurate laser positioning system (ALPS) is an emerging distributed large-scale laser positioning system, which has shown broad prospects in industrial production, navigation, and other fields. Addressing the issue of reduced measurement accuracy in complex large-space measurement scenarios due to laser signal obstruction encountered by traditional planar and spherical receiver units, this paper analyzes the measurement principle of ALPS. Based on omnidirectional photoelectric array units, a comprehensive vector measurement model is established, and a position and attitude calculation method for omnidirectional vector measurement is studied. Employing the Monte Carlo method based on statistical principles, the feasibility of the position and attitude calculation method is verified through simulation. Additionally, field experiments are conducted in conjunction with a laser tracking system to evaluate the absolute accuracy of the proposed model under point and attitude changes. The results indicate that the point accuracy exceeds 0.24mm with a standard deviation of less than 0.026mm. When attitude changes occur, the horizontal and pitch directions are superior to 0.28mm and 0.47mm, respectively, with standard deviations of less than 0.030mm and 0.032mm, meeting the system's sub-millimeter-level requirements and expanding the application scope of the ALPS system.

关键词： Accuracy Monte Carlo methods Measurement by laser beam Receivers Position measurement Laser stability Laser modes Vectors Laser fusion Standards

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Disturbance Observer Based on Terminal Sliding-Mode control for a Secure Communication of Fractional-Order Takagi-Sugeno Fuzzy Chaotic Systems

Disturbance Observer Based on Terminal Sliding-Mode Control ...

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International Conference on Advanced Mechanical engineering, automation and Sustainable Development, AMAS 2021

作者： Giap, Van Nam Nguyen, Quang Dich Trung, Nguyen Kien Huang, Shyh-Chour Trinh, Xuan Thang Institute for Control Engineering and Automation Hanoi University of Science and Technology Hanoi100000 Viet Nam School of Electrical Engineering Hanoi University of Science and Technology Hanoi100000 Viet Nam Department of Mechanical Engineering National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan Faculty of Mechanical Engineering Hung Yen University of Technology and Education Hung Yen160000 Viet Nam

ISBN: (纸本)9783030996659

In this study, the disturbance observer (DOB) for a secure communication system (SCS) of the fractional-order (FO) Takagi-Sugeno (T-S) fuzzy systems was newly proposed. The synchronization of the master and slave systems (MSS) was guaranteed by a terminal sliding mode control (TSMC) method. The experimental study of computer communication-based MATLAB software with support of FOMCON toolbox was used to confirm the correction of the proposed theories. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Disturbance observer Fractional order Secure communication T-S fuzzy chaotic system Terminal sliding mode control

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Evolving temporal convolutional neural networks for modeling turntable servo systems

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Applied Soft Computing 2025年 172卷

作者： Xie, Cheng Xue, Bing Zhang, Mengjie Chen, Songlin Liu, Yang Control and Simulation Center School of Astronautics Harbin Institute of Technology Heilongjiang Harbin150001 China Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110169 China

Turntable servo systems are important experimental equipment used for semi-physical simulation and testing of aircraft, which have very strict requirements for tracking performance. To achieve high-precision servo performance, some advanced model-based control methods have been used recently, but the control performance is greatly influenced by the accuracy of the model. Therefore, this paper proposes evolving temporal convolutional networks (TCNs) for accurately modeling turntable servo systems. Considering the advantages of TCNs in capturing local dependencies in sequence data and parallel computing, a TCN is adopted to compensate the unmodeled dynamics to significantly improve the accuracy of the existing dynamics model. Then, an evolutionary algorithm (EA) based architecture optimization algorithm for the above TCN model is proposed, and the network performance is continuously improved by simulating the population evolution in nature. Finally, the weight inheritance method is utilized to evaluate the performance of individuals during the evolution process to effectively improve the execution efficiency of the whole algorithm. In addition, the subnets inherited from the supernet meet the real-time requirements of the actual tasks by setting real-time constraints on the supernet. The experimental results demonstrate the superiority of the proposed model against the peer competitors in terms of the prediction performance. In addition, the proposed optimization algorithm also shows higher execution efficiency as verified by ablation experiments. © 2025 Elsevier B.V.

关键词： Evolutionary algorithms (EAs) System identification and modeling Temporal convolutional networks (TCNs) Turntable servo systems

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Tracing segmentation for satellite partial components under low-light environment 2

Tracing segmentation for satellite partial components under ...

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2nd International Conference on Computer Vision, Image, and Deep Learning

作者： Xu, Zhuang Zhang, Yang Gao, Jingmin Zhang, Kebei Guo, Miao School of Automation Beijing Information Science and Technology University Beijing100192 China Beijing Institute of Control Engineering Beijing100190 China

ISBN: (数字)9781510646827

ISBN: (纸本)9781510646810

The lack of lighting in the space environment results in low segmentation accuracy and target lost. To solve this problem, a satellite component tracking method based on Few-Shot learning is proposed in this paper. First, we design a convolutional neural network, which inputs the first frame of mask information, and outputs the true label and important weight parameters. The Few-Shot learning incorporates the real labels, important weight parameters and the first frame feature information to generate target model parameters. Subsequent frames combine target model parameters with feature extraction, and finally output target mask after encoding and decoding. Our algorithm is evaluated on a new satellite partial component data set, and the simulation results show that the proposed method improves the segmentation accuracy and reduces the target loss rate compared to SiamMask under low-light environment. © 2021 SPIE.

关键词： Satellites

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ChatSTL: A Framework of Translation from Natural Language to Signal Temporal Logic Specifications for Autonomous Vehicle Navigation out of Blocked Scenarios

ChatSTL: A Framework of Translation from Natural Language to...

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International Conference on Computer and automation engineering, ICCAE

作者： Yujin Wang Zhaoyan Huang Shiying Dong Hongqing Chu Xiang Yin Bingzhao Gao School of Automotive Studies Tongji University Shanghai China Department of Control Science and Engineering Jilin University Changchun China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350370058

ISBN: (纸本)9798350370164

This paper addresses the quandary that autonomous vehicles may encounter in blocked scenarios, and proposes a novel framework of translation from natural language to Signal Temporal Logic specifications based on Large Language Model, which helps autonomous vehicles navigate out of blocked scenarios through path planning. We utilize the profound capability of reasoning and planning of GPT-3.5, design a set of predicate functions and formulate the prompts in order to generate accurate Signal Temporal Logic specifications describing driving tasks. The feasibility of the proposed framework is demonstrated via a numerical experiment.

关键词： Navigation Computational modeling Natural languages Path planning Cognition Planning Numerical models

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Bilevel Evolutionary Multi-objective Algorithm with Multiple Lower-level Search Modes

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IEEE Transactions on Evolutionary Computation 2025年

作者： Yang, Ning Liu, Hai-Lin Guangdong University of Technology School of Automation Ganzhou China Jiangxi University of Science and Technology Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control School of Information Engineering Ganzhou China Guangdong University of Technology Center for Mathematics and Interdisciplinary Sciences School of Mathematics and Statistics Guangzhou China

In bilevel optimization, the upper-level optimization problem (ULOP) requires to be solved under the constraint of the inner lower-level optimization problem (LLOP). However, it is computationally expensive to always consider the constraint caused by the LLOP in a higher priority because heavy evaluation budgets are required for validating the constraint satisfaction. From this aspect, this paper investigates bilevel evolutionary multi-objective optimization with multiple lower-level search modes (BLEMO-MLS). Assisted by self-learning and reinforcement, BLEMO-MLS could adaptively adjust the priority of considering more on the upper-level objective optimization or the constraint caused by the LLOP. In BLEMO-MLS, three lower-level search modes are designed to handle the constraint caused by the LLOP. The former two search modes have higher priorities on the satisfaction of the constraint caused by the LLOP, where lower-level decisions of solutions are optimized by a hybrid lower-level search method, while the last search mode has a higher priority on the upper-level objective optimization with the constraint caused by the LLOP temporarily ignored. Through self-learning and reinforcement, BLEMO-MLS dynamically selects proper lower-level search modes in the bilevel optimization process, aiming to obtain approximate bilevel Pareto-optimal solutions fulfilling the constraint caused by the LLOP as much as possible. Compared with five existing bilevel evolutionary algorithms, BLEMO-MLS could effectively solve bilevel multi-objective optimization problems with function evaluations saved in both levels. © 1997-2012 IEEE.

关键词： Optimization algorithms

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