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Data-driven control of consensus tracking for discrete-time multi-agent systems

Journal of the Franklin institute 2023年第7期360卷 4661-4674页

作者： Xiufeng Zhang Gang Wang Jian Sun Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming 650504 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming 650504 China School of Automation Beijing Institute of Technology Beijing 100081 China Key Lab of Intelligent Control and Decision of Complex Systems Beijing 100081 China

This paper investigates the consensus tracking problem of leader-follower multi-agent systems. Different from most existing works, dynamics of all the agents are assumed completely unknown, whereas some input-output data about the agents are available. It is well known from the Willems et al. Fundamental Lemma that when inputs of a linear time-invariant (LTI) system are persistently exciting, all possible trajectories of the system can be represented in terms of a finite set of measured input-output data. Building on this idea, the present paper proposes a purely data-driven distributed consensus control policy which allows all the follower agents to track the leader agent’s trajectory. It is shown that for a linear discrete-time multi-agent system, the corresponding controller can be designed to ensure the global synchronization with local data. Even if the data are corrupted by noises, the proposed approach is still applicable under certain conditions. Numerical examples corroborate the practical merits of the theoretical results.

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Investigating the dynamic memory effect of human drivers via ON-LSTM

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Science China(Information Sciences) 2020年第9期63卷 83-93页

作者： Shengzhe DAI Zhiheng LI Li LI Dongpu CAO Xingyuan DAI Yilun LIN Department of Automation Tsinghua University Shenzhen International Graduate School Tsinghua University Department of Mechanical and Mechatronics Engineering University of Waterloo State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

It is a widely accepted view that considering the memory effects of historical information(driving operations) is beneficial for vehicle trajectory prediction models to improve prediction accuracy. However,many commonly used models(e.g., long short-term memory, LSTM) can only implicitly simulate memory effects, but lack effective mechanisms to capture memory effects from sequence data and estimate their effective time range(ETR). This shortage makes it hard to dynamically configure the most suitable length of used historical information according to the current driving behavior, which harms the good understanding of vehicle motion. To address this problem, we propose a modified trajectory prediction model based on ordered neuron LSTM(ON-LSTM). We demonstrate the feasibility of ETR estimation based on ON-LSTM and propose an ETR estimation method. We estimate the ETR of driving fluctuations and lane change operations on the NGSIM I-80 dataset. The experiment results prove that the proposed method can well capture the memory effects during trajectory prediction. Moreover, the estimated ETR values are in agreement with our intuitions.

关键词： driving behavior memory effect trajectory prediction historical information ON-LSTM

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Design and locomotion characteristic analysis of a novel tensegrity hopping robot*

Design and locomotion characteristic analysis of a novel ten...

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IEEE International Conference on Robotics and Biomimetics

作者： Jixue Mo Changqing Gao Hao Fang Qingkai Yang Department of Strategic and Advanced Interdisciplinary Research Peng Cheng Laboratory Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China

In consideration of the poor locomotion ability of most traditional tensegrity robot, a novel tensegrity hopping robot powered by push-pull electromagnets was proposed with better locomotivity. It is able to conduct stable consecutive progressing action and turning action through hopping. This paper covers the structural design, theoretical modeling of the robot’s hopping process, as well as its self-righting analysis, simulation and experimental verification. The average moving speed and relative moving speed were measured to be 0.334m/s and 0.641 body length/s respectively, which obviously surpass the general moving ability of most traditional tensegrity robot. In addition, the robot was also validated to have good self-righting characteristic for stable consecutive hopping. Thus this robot can be potentially applied for search, rescue, detection, etc. in outdoor and unstructured environment.

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Adaptive Distributed Cluster Flow control for a Group of Autonomous Robots*

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IFAC-PapersOnLine 2023年第2期56卷 8690-8695页

作者： V. Erofeeva V. Ershov O. Granichin V. Pankov D. Uzhva O. Granichina Laboratory “Control of Complex Systems” Institute for Problems in Mechanical Engineering V.O. Bolshoy 61 199178 Saint Petersburg Faculty of Mathematics and Mechanics Research Laboratory for Analysis and Modeling of Social Processes Saint Petersburg State University 7/9 Universitetskaya Emb. 199034 Saint Petersburg Institute of Childhood Herzen State Pedagogical University 48 Moika Embankment 191186 Saint Petersburg

Conventional multiagent control strategies rely either on pre-defined operational patterns, or complex inter-agent communications. While the former provides simple yet compelling results, the latter allows to achieve goals in complex environments with unpredictable disturbances. However, designing local networked control solutions for large-scale robotic systems is extremely challenging. A novel way to control complex systems by manipulating system clusters as sub-systems is proposed. The relation between sparsity and cluster flows is manifested in the new developed approach to estimate cluster structure of a group of robots based only on a few local observations of their states. Aiming to perform necessary measurements of all N agent states, the compressed sensing methodology is used to obtain ∼ logN randomized aggregated measurements, followed by the corresponding series of local voting protocols for the network to reach a common aggregated state with randomized weights. Thus, the cluster control synthesis by these compressed measurements is computationally efficient, and yet precise. Effectiveness of the proposed method is illustrated in numeric simulations, where adaptive observation feedback cluster control outperforms conventional strategies.

关键词： Adaptive control of multi-agent systems Networked robotic systems Distributed optimization for large-scale systems

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On the Notch Sensitivity of Nacre: A Large-Scale Numerical Investigation

SSRN

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SSRN 2024年

作者： Yan, Yi Chen, Xiao Gao, Yuan Deng, Zi-Chen Feng, Xi-Qiao Department of Engineering Mechanics MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China Department of Materials Science and Technology National University of Singapore 117575 Singapore School of Mechanical Engineering Hebei University of Technology Tianjin300401 China Institute of Biomechanics and Medical Engineering AML Department of Engineering Mechanics Tsinghua University Beijing100084 China

The brick–mortar microstructure of nacre endows it with remarkable strength and fracture toughness. Notably, nacre demonstrates robustness against flaws and minor cracks, although its microstructural mechanisms remain elusive. A significant challenge is that the fracture behavior of nacre spans a wide range of length scales, from the mineral platelet less than 1 μm to a damage process zone extending several millimeters. In this study, we employ a multiscale tension-shear-chain (TSC) network model to simulate the large-scale fracture process of nacre's brick-and-mortar structure. In particular, we investigate how the fracture behavior of nacre is influenced by notches of varying sizes. Our large-scale simulations show that with the decrease in the preexisting-crack length, an independent crack initiation gradually replaces the precrack propagation during the fracture of nacre. This observation confirms the critical transition of fracture from the process dominated by fracture toughness to the one by strength, namely from notch sensitivity to insensitivity. Further investigations are carried out on the dependence of this fracture transition on constituent and microstructural parameters, including the interfacial cohesive strength and microstructural randomness. We also propose a scaling law formulating the relationship between these parameters and the notch sensitivity, which provides insight into nacre’s tolerance to flaws at a microscale level. Our findings could guide the design of nacre-inspired artificial materials for fracture prevention. © 2024, The Authors. All rights reserved.

关键词： Fracture toughness

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Mechanics and Thermodynamics of Multivalent-Binding Induced Shrinkage of Hydrogels

SSRN

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SSRN 2024年

作者： Rajendran, Barathan Chen, Xiao Wang, Xin Wu, Tao Goh, Kek Boon Department of Mechanical Engineering School of Engineering Monash University Malaysia Jalan Lagoon Selatan Bandar Sunway Selangor 47500 Malaysia Institute of Biomechanics and Medical Engineering Department of Engineering Mechanics Tsinghua University Beijing100084 China School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Xi’an710072 China

Understanding the fundamental mechanism of shrink hydrogel sensors necessitates a complete comprehension of analyte-centered multivalent binding that occurs within their salt-rich microenvironments. However, the mechanics and thermodynamics governing this phenomenon remain insufficiently understood. Here, we derive a theoretical framework that examines the formation of temporary cross–links between the polymeric chains induced by the specific binding interaction between the fixed receptors and the multivalent analytes. As a highlight of our theory, we use statistical thermodynamics to characterize the analyte transport and these multivalent complexations, allowing us to subsequently determine the mechanics of the shrinkage experienced by these hydrogel sensors. Consequently, our theory unveils the valency of the bindings, setting an upper bound for the performance of these shrink sensors;i.e., tuning the receptor density allows us to further modulate their performance. In summary, our theory tightly correlates the microscopic properties of the analyte and hydrogel to the macroscopic shrinking properties of these sensors, hence facilitating a structured design and optimization regime for advanced biomedical applications. © 2024, The Authors. All rights reserved.

关键词： Shrinkage

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Photovoltaic Arrays’ Dynamic Model Parameter Estimation

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IFAC-PapersOnLine 2023年第2期56卷 6845-6850页

作者： Alexey Bobtsov Fernando Mancilla-David Stanislav Aranovskiy Romeo Ortega Faculty of Control Systems and Robotics ITMO University 197101 Saint Petersburg Russia Laboratory “Control of Complex Systems” Institute of Problems of Mechanical Engineering V.O. Bolshoj pr. 61 St. Petersburg 199178 Russia University of Colorado Denver Denver Colorado 80204 USA IETR – CentaleSupélec Cesson-Sévigné France Departamento Académico de Sistemas Digitales ITAM México

This paper deals with the problem of on-line identification of the parameters of a realistic dynamical model of a photovoltaic array connected to a power system through a power converter. It has been shown in the literature that, when interacting with switching devices, this model is able to better account for the PV array operation, as compared to the classical five parameter static model of the array. While there are many results of identification of the parameters of the latter model, to the best of our knowledge, no one has provided a solution for the aforementioned more complex dynamic model since it concerns the parameter estimation of a nonlinear, underexcited system with unmeasurable state variables. Achieving such objective is the main contribution of the paper. We propose a new parameterisation of the dynamic model, which, combined with the powerful identification technique of dynamic regressor extension and mixing, ensures a fast and accurate online estimation of the unknown parameters. Realistic numerical examples via computer simulations are presented to assess the performance of the proposed approach—even being able to track the parameter variations when the system changes operating point.

关键词： photovoltaic arrays on-line parameter estimation identification of nonlinear systems

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Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

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Chinese Journal of Aeronautics 2020年第2期33卷 427-438页

作者： Yongbo LI Xiaoqiang DU Fangyi WAN Xianzhi WANG Huangchao YU MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical UniversityXi’an 710072China School of Mechanical Engineering Northwestern Polytechnical UniversityXi’an 710072China Institute of Unmanned System National University of Defense TechnologyChangsha 410073China

Rotating machinery is widely applied in industrial *** diagnosis of rotating machinery is vital in manufacturing system,which can prevent catastrophic failure and reduce financial ***,Deep Learning(DL)-based fault diagnosis method becomes a hot *** Neural Network(CNN)is an effective DL method to extract the features of raw data *** paper develops a fault diagnosis method using CNN for InfRared Thermal(IRT)***,IRT technique is utilized to capture the IRT images of rotating ***,the CNN is applied to extract fault features from the IRT *** the end,the obtained features are fed into the Softmax Regression(SR)classifier for fault pattern *** effectiveness of the proposed method is validated using two different experimental *** show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.

关键词： Convolutional neural network Feature extraction Infrared thermography(IRT) Intelligent fault diagnosis Rotating machinery

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Large Language Model-Driven Urban Traffic Signal control

Large Language Model-Driven Urban Traffic Signal Control

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control Conference (ANZCC), Australian and New Zealand

作者： Yiqing Tang Xingyuan Dai Chen Zhao Qi Cheng Yisheng Lv School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Mechanical and Electrical Engineering China University of Mining and Technology at Beijing University of Mining and Technology (Beijing) Inner Mongolia Research Institute Ordos China

In recent years, large language models (LLM) have received a lot of attention for their ability to understand, generate and process natural language. By fine-tuning the models on specific domains or using prompts, LLMs can generate summaries tailored to different contexts and requirements. We hope to utilize these advantages of LLM in urban traffic signal control and provide a new paradigm for traffic control. In this paper, we propose a large language model-driven urban traffic control method, which is designed based on artificial systems, computational experiments, parallel execution (ACP) method. There are three traffic control modes, autonomous mode, feedback mode, and human takeover mode. In autonomous mode, we adopt recommendations based on inventory strategy, which can to some extent reduce the acquisition time of traffic control strategies. With the continuous construction and improvement of the strategic library, the efficiency of the system will be further improved. The feedback mode is mainly aimed at traffic scenarios that are not in the library, generating new control strategies and introducing human feedback. The human takeover model ensures that human engineers have direct control over the actual transportation system. LLM drives the interaction between human engineers and computers in the process of traffic control. Assist human engineers in achieving traffic control and improving work efficiency.

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Compressed Cluster Sensing in Multiagent IoT control

Compressed Cluster Sensing in Multiagent IoT Control

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IEEE Conference on Decision and control

作者： Denis Uzhva Oleg Granichin Olga Granichina Laboratory "Control of Complex Systems" Institute for Problems in Mechanical Engineering Saint Petersburg Department of Software Engineering Mathematics and Mechanics Saint Petersburg State University Saint Petersburg Herzen State Pedagogical University Saint Petersburg

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Traditional multiagent system control relies primarily on inter-agent local communications. In large-scale IoT systems it may appear hard to synthesize local control actions in a simple manner. Cluster control possibilities are thus thoroughly discussed, with possible control goals stated. The relation between multiagent state sparsity and cluster patterns is illustrated, for further utilization of sparsity for cluster control. Consequently, the problem of cluster identification is stated, and a possible solution is proposed in the form of the compressed cluster sensing algorithm. The algorithm utilizes compressed sensing methodology for a compact representation of agent states, which is then used to synthesize compact control actions in a low-dimensional space, without requiring to specify cluster locations.

关键词： Wireless sensor networks Heuristic algorithms Clustering algorithms Sensors Numerical models Nonlinear dynamical systems Synchronization

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