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43rd Chinese control Conference, CCC 2024

作者： Sun, Tao Liang, Haili Yu, Xiaotong Zhou, Zhao Xu, Yang Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai200444 China East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Shanghai200237 China

ISBN: (纸本)9789887581581

Human societies and natural environments form a complex ecological system, in which human activities can change the ecological environment, the changes in the ecological environment can influence human social activities in reverse. This paper introduces a punitive mechanism as environmental feedback into co-evolutionary systems, and proposes a replicator dynamic model which aims to explain the evolutionary strategies adopted by populations for the multiplayer snowdrift games. We obtain the necessary and sufficient condition for the existence and stability of the internal equilibrium point by studying on the Jacobian matrix, and the effectiveness of the theoretical results is examined by numerical simulations. We identify that the threshold parameter in multiplayer snowdrift games determines the evolution of the closed-loop system. Specifically, the system exhibits limit cycles when the threshold is small enough. As the threshold increases, the system converges to a stable internal equilibrium point. Finally, if the threshold reaches a sufficiently large value, the proportion of cooperators in the population is high, while the group of punishers becomes extinct, indicating the loss of environmental feedback. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Anthropogenic

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Unmanned Air-Ground Vehicles Path Planning Using Model Predictive control

Unmanned Air-Ground Vehicles Path Planning Using Model Predi...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Gu, Yuanwen Wang, Mi Wu, Huaining Li, Chenlong Li, Gang School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Hangzhou International Innovation Institute Beihang University Hangzhou311115 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China Department of Control Science and Engineering Tongji University Shanghai201800 China

ISBN: (纸本)9789819639649

In this article, the problem of air-ground vehicles path planning is investigated. The objective is to navigate each vehicle from its starting position to the intended destination, while avoiding collisions with obstacles and other vehicles. First, by combining the objective functions of unmanned ground vehicle (UGV) and unmanned aerial vehicles (UAVs) with the constraints of system equations, control constraints and obstacle avoidance constraints, the vehicles’ path planning is formulated as multiple model predictive control (MPC) problems. Second, considering the interdependencies inherent in these optimization problems, we employ an assumed state method to decouple them effectively. Third, the control inputs of the UGV and UAVs are obtained by solving quadratic programming problems and thus the vehicles’ path can be obtained. Finally, a simulation example is shown to illustrate the effectiveness of the proposed path planning method. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Predictive control systems

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Harmonic Disturbance Observer-Based Composite controller of Gimbal Servo System under Multiple Disturbances 5

Harmonic Disturbance Observer-Based Composite Controller of ...

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5th International Symposium on Autonomous Systems, ISAS 2022

作者： Cui, Yangyang Zhao, Lei Zhu, Yukai Yang, Yongjian Zhou, Changsheng Qiao, Jianzhong Beihang University School of Automation Science and Electrical Engineering Beijing China School of Automation Beijing Institute of Technology Beijing Institute of Control Engineering Beijing China School of Astronautics Beihang University Beijing China

ISBN: (纸本)9781665487085

For the gimbal servo system, multiple disturbances restrict the improvement of its angular velocity tracking accuracy. Aiming at solving this problem, a harmonic disturbance observer-based composite controller is developed in this paper. Firstly, multiple disturbances are modeled, Fourier analyzed, and classified based on frequency distribution characteristics. Secondly, a robust control method is designed based on loop-shaping technology for the gimbal servo system to ensure servo performance and suppress low-frequency disturbances. To estimate and compensate the imbalance vibration, a harmonic disturbance observer (HDO) is proposed according to the frequency information of imbalance vibration. Finally, numerical simulations are carried out to evaluate the effectiveness of the proposed method. © 2022 IEEE.

关键词： Robust control

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Bearing Remaining Useful Life Prediction Combining Trajectory Similarity and Relevance Vector Machine 41

Bearing Remaining Useful Life Prediction Combining Trajector...

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第41届中国控制会议

作者： Zhuyi Li Kesen Fan Cong Zhang Sihang Zhang Yiming Wan Key Laboratory of Image Processing and Intelligent Control Ministry of Education Chinaand School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Institute of Control Engineering

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Bearing remaining useful life(RUL) prediction is critical for safe operation of rotating *** this paper,we propose a combined RUL prediction approach that leverages both trajectory similarity and relevance vector machine(RVM).The similarity based prediction relies on historical degradation trajectories that are highly similar to the online data,hence would perform poorly if all historical trajectories have low similarity with the online *** RVM based prediction relies solely on a regression model learned from the available online data,thus gives an inaccurate prediction when insufficient data are available in the early stage of degradation.A weighted sum of these above two predictions is proposed to address the limitation of each single prediction method,whose weights are determined by solving a non-negative least squares fitting *** further improve RUL prediction accuracy,we distinguish between fast and slow degradation modes,so that each mode uses a different set of historical degradation trajectories and kernel *** doing so,we predict RUL under the identified *** case study using the PHM2012 dataset demonstrates the effectiveness of the proposed RUL prediction approach.

关键词： Bearing degeneration remaining useful life prediction relevance vector machine trajectory similarity

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SKE-MSA: Enhancing Representation Learning with VAD Lexicon for Multimodal Sentiment Analysis

SKE-MSA: Enhancing Representation Learning with VAD Lexicon ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yang Yang Xunde Dong Yupeng Qiang Wenjie Si School of Automation Science and Engineering South China University of Technology School of Electrical and Control Engineering Henan University of Urban Construction

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Most existing Multimodal Sentiment Analysis (MSA) models that are enhanced with external sentiment knowledge primarily focus on integrating this knowledge during the multimodal fusion stage, while overlooking its potential benefits during the multimodal representation learning process. In this study, we propose a Sentiment Knowledge-Enhanced Multimodal Sentiment Analysis (SKE-MSA) framework, which incorporates an external sentiment knowledge base—the VAD lexicon—to enhance MSA. SKE-MSA introduces the Sentiment-aware Encoding Loss (SAE_Loss), designed to leverage external sentiment knowledge to guide the representation learning of multimodal encoders. Building on this, we extract both common and unique features of multimodal representations and subsequently predict sentiment intensity based on these features. Extensive experiments conducted on three MSA datasets demonstrate the competitive performance of SKE-MSA.

关键词： Representation learning Sentiment analysis Knowledge based systems Buildings Lexicon Signal processing Feature extraction Encoding Data mining Speech processing

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Reinforcement learning-enhanced dynamic multi-objective evolutionary algorithm for cooperative shovel scheduling in open-pit mining

Reinforcement learning-enhanced dynamic multi-objective evol...

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2024 China automation Congress, CAC 2024

作者： Zhang, Zihao Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Artificial Intelligence Anhui University of Science & Technology Hefei232001 China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350368604

Aiming at the ore blending problem in the open-pit mining scenario, the optimization objective is to minimize the grade variance of the sliding window of the ore flow, and a cooperative shovel scheduling model for real-time ore blending is established. A reinforcement learning-enhanced dynamic multi- objective evolutionary algorithm is proposed, where a Q-learning operator selection mechanism is introduced to reuse the information from the previous environment for tracking the optimal solution in the dynamic environment. Experimental results show that the proposed method can effectively control the grade fluctuation of the ore flow and dynamically respond to external events, and can find a better solution in real time compared with the traditional operator selection algorithm. © 2024 IEEE.

关键词： Open pit mining

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Nussbaum-based distributed containment control for nonlinear multi-agent systems with quantized inputs

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IEEE Transactions on control of Network Systems 2024年

作者： Liu, Yang Zhang, Jiaming Seventh Research Division The Center for Information and Control School of Automation Science and Electrical Engineering Beijing100191 China

This article explores the distributed containment control problem for uncertain nonlinear multi-agent systems subject to external disturbances and input quantization, where the constant control gains and the upper bounds of the external disturbances are unknown. First, the reference generator is constructed for each follower agent to generate the virtual tracking signal, confining to the convex hull spanned by the leaders. Meanwhile, the unmeasurable state variable of each follower is estimated by adopting the K-filters based on the available output/input signals and known system function matrices. Then, a distributed adaptive output feedback controller with only one updating parameter is designed for each follower by introducing a logarithmic quantization to quantize the control inputs under the framework of prescribed performance control. The lack of priori knowledge for the control gain and the quantization gain is counteracted effectively by employing the Nussbaum function method in the adaptive backstepping design process. It is proved that the outputs of followers can enter into the convex hull of multiple leaders and the relevant tracking errors satisfy the prescribed performance index. Finally, the validity of the proposed schemes is illustrated through simulation studies on robotic systems. © 2014 IEEE.

关键词： Backstepping

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RESEARCH OF THE CHARACTERISTICS OF THE ACTIVE LOAD-SENSITIVE DRIVE SYSTEM

RESEARCH OF THE CHARACTERISTICS OF THE ACTIVE LOAD-SENSITIVE...

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2024 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024

作者： Mou, Quanrun Liu, Xiaochao Wang, Zhenyu Qiu, Zhongyi Li, Juefei Jiao, Zongxia School of Aeronautic Science and Engineering Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Ningbo Institute of Technology Beihang University Ningbo China Science and Technology on Aircraft Control Laboratory Beihang University Beijing China Tianmushan Laboratory Xixi Octagon City Yuhang District Hangzhou310023 China

In this paper, an active load-sensitive valve-controlled variable displacement motor drive system is introduced, aiming at solving the problem of large energy waste in valve-controlled fixed-displacement system. The fixed-displacement system is unable to match the variable load conditions promptly due to the fixed displacement, resulting in serious throttling losses under small load conditions. This study analyses the energy consumption characteristics by establishing a mathematical model of the active load-sensitive drive system. In addition, the nonlinear mapping of displacement and load pressure difference is designed, and simulation verification of comparing active load-sensitive drive system with the valve-controlled fixed-displacement motor drive system is completed. By analysing the simulation results, it can be seen that the active load-sensitive drive system can reduce the average flow rate by 68% and the peak flow rate by nearly 66% under the small load condition, which effectively solves the problem of heavy throttling loss in the valve-controlled hydraulic system. © The Institution of engineering & Technology 2024.

关键词： Energy utilization

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Dynamic thermal coupling modeling and analysis of wet electro-hydrostatic actuator

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Chinese Journal of Aeronautics 2022年第6期35卷 298-311页

作者： Zongxia JIAO Yanpeng LI Tian YU Chaofan JIANG Ligang HUANG Yaoxing SHANG School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China Science and Technology on Aircraft Control Laboratory Beihang UniversityBeijing 100083China Research Institute for Frontier Science Beihang University100083 BeijingChina Ningbo Institute of Technology Beihang UniversityNingbo 315800China

The electro-hydrostatic actuator(EHA)used in more electric aircraft(MEA)has been extensively studied due to its advantages of high reliability and high ***,this high integration results in a small heat dissipation area,leading to high-temperature ***,to reduce the temperature,a wet cooling method of using the pump leakage oil to cool the motor is adopted,which can also increase the difficulty of accurately predicting the system temperature in the early design *** solve this problem,a dynamic coupling thermal model of a wet EHA is proposed in this *** particular,the leakage oil of the pump is used as a coupling item between the electrical system and the hydraulic ***,an improved T-equivalent block model is proposed to address the uneven distribution of axial oil temperature inside the motor,and the control node method is applied to hydraulic system thermal ***,a dynamic coupling thermal model is developed that enables a dynamic evaluation of the wet EHA ***,experimental prototypes of wet motor and wet EHA are developed,while the temperature response of the wet motor at different rotation speeds and different loads and the temperature response of the wet EHA at no-load condition were verified experimentally at room temperature,*** maximum temperature difference between the experimental and theoretical results of the wet motor as well as the experimental and theoretical results of the wet EHA is less than 8℃.These test results indicate that the dynamic coupling thermal model is valid and demonstrate that the thermal coupling modeling method proposed in this paper can provide a basis for the detailed thermal design of EHA.

关键词： Electro-hydrostatic actuator Hydraulic More electric aircraft Thermal mode Wet motor

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Learning Effective Communication for Cooperative Pursuit with Multi-Agent Reinforcement Learning

Learning Effective Communication for Cooperative Pursuit wit...

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2022 Chinese automation Congress, CAC 2022

作者： Deng, Yubang Cao, Xianghui Yang, Qinmin Southeast University School of Automation Nanjing China Southeast University School of Cyber Science and Engineering Nanjing China Zhejiang University College of Control Science and Engineering Hangzhou China

ISBN: (纸本)9781665465335

In a multi-agent environment, agents need to frequently communicate with each other. However, due to resource constraints such as limited bandwidth, they may not be able to keep broadcasting messages. Therefore, agents are required to analyze the importance of their local observations and broadcast their messages only when necessary. To address these issues, we propose a multi-agent reinforcement learning algorithm with communication called Proximal Policy Optimization with Gated Attention Communication (PPO-GAC). It follows the Centralized Training and Decentralized Execution (CTDE) framework and is capable of deciding when to broadcast and how to handle messages received from other agents. Furthermore, we evaluate our algorithm in a multi-agent pursuit task. The simulation result shows that pursuers with PPO-GAC have the best performance in capturing all evaders compared to other baseline algorithms. © 2022 IEEE.

关键词： Reinforcement learning

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