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Learning-Based Predictive control of Switched Nonlinear Systems via Koopman Modeling Approach

Journal of Systems Science & Complexity 2025年第2期38卷 691-716页

作者： NING Zepeng FANG Xu LI Yibei XIE Lihua School of Electrical and Electronic Engineering Nanyang Technological University Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences

This paper proposes a data-driven learning-based approach to predictive control for switched nonlinear systems subject to state and control constraints and external stochastic disturbances. A switched Koopman modeling framework is developed, where a multi-mode neural network for state lifting is trained simultaneously with Koopman operators and state reconstruction matrices for all *** framework facilitates the construction of the switched linear Koopman model in a transformed space and effectively captures the dynamics of the original nonlinear system. A switched predictive control strategy is then designed to regulate the switched Koopman model with constrained states and control inputs against both the stochastic disturbances and the uncertainties introduced by the lifting neural network. The proposed control scheme ensures mean-square stability and guarantees boundedness during the online phase. Furthermore, boundedness analysis is performed to determine the bounded set of the original system state across all admissible switching sequences. The effectiveness of the proposed methodology is demonstrated through a case study of a gene regulatory network.

关键词： Data-driven machine learning model predictive control stochastic systems switched Koopman model switched nonlinear systems

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Event-Triggered Switched Observers for Networked Distributed Multi-Agent Systems via Cloud Computing 6

Event-Triggered Switched Observers for Networked Distributed...

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Zhang, Chen Ren, Hongru Ma, Hui Zhou, Qi School of Automation Guangdong University of Technology Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China School of Mathematics and Statistics Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9798350353594

This paper designs event-triggered switched observers for the networked distributed multi-agent systems based on the cloud computing systems. Firstly, a novel cloud computing system architecture is designed, in which each group of agents can interact with each others through distributed cloud nodes, and each cloud node also serves as an event-triggered switched observer to observe the topologies of the systems. Secondly, event-triggered switched observers are designed, which can assist distributed agents in accurately obtaining information from the leader, even with continuous switching topologies. Then, through stability analysis, we demonstrate the effectiveness of the observers. Finally, we prove the feasibility of the observers through simulation. © 2024 IEEE.

关键词： Distributed cloud

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Event-based performance guaranteed tracking control for constrained nonlinear system via adaptive dynamic programming method

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Journal of Automation and Intelligence 2023年第4期2卷 239-247页

作者： Xingyi Zhang Zijie Guo Hongru Ren Hongyi Li School of Automation and Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of TechnologyGuangzhou 510006China

An optimal tracking control problem for a class of nonlinear systems with guaranteed performance and asymmetric input constraints is discussed in this *** control policy is implemented by adaptive dynamic programming(ADP)algorithm under two event-based triggering *** is often challenging to design an optimal control law due to the system deviation caused by asymmetric input ***,a prescribed performance control technique is employed to guarantee the tracking errors within predetermined ***,considering the asymmetric input constraints,a discounted non-quadratic cost function is ***,in order to reduce controller updates,an event-triggered control law is developed for ADP *** that,to further simplify the complexity of controller design,this work is extended to a self-triggered case for relaxing the need for continuous signal monitoring by hardware *** employing the Lyapunov method,the uniform ultimate boundedness of all signals is proved to be ***,a simulation example on a mass–spring–damper system subject to asymmetric input constraints is provided to validate the effectiveness of the proposed control scheme.

关键词： Adaptive dynamic programming(ADP) Asymmetric input constraints Prescribed performance control Event-triggered control Optimal tracking control

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Linear Quadratic Pursuit and Evasion Differential Game Guidance Strategy with Obstacle Avoidance 34th

Linear Quadratic Pursuit and Evasion Differential Game Guida...

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34th Congress of the International Council of the Aeronautical Sciences, ICAS 2024

作者： Wang, Xintao Yang, Ming Ma, Ping Chao, Tao Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Harbin150080 China

In this paper, a class of linear quadratic differential game guidance scheme is presented to study the pursuit and evasion conflict scenario that an evader establishes a static obstacle and performs the evasion maneuver to avoid a pursuer. First of all, the engagement kinematic model among the obstacle, the pursuer, and the evader is linearized according to the assumption. The issue of the pursuit and evasion game is transformed into a linear quadratic differential game through the dead zone and the cost functions. Furthermore, the linear quadratic differential game approach is utilized to obtain the guidance strategy with obstacle avoidance. Numerical simulation results are employed to validate the performance of the guidance policy. © 2024, International Council of the Aeronautical Sciences. All rights reserved.

关键词： Cost functions

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Airfoil Aerodynamic Force Prediction Based on Graph Neural Networks 43

Airfoil Aerodynamic Force Prediction Based on Graph Neural N...

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43rd Chinese control Conference, CCC 2024

作者： Wang, Guoqing Cao, Junzhe Dalian University of Technology Control Science and Engineering Dalian116024 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China

ISBN: (纸本)9789887581581

Aerodynamic force prediction is an important application of deep learning in the field of fluid mechanics. In this paper, a light graph neural network model with residual connections named LRGCN is designed to speed the computational process of the aerodynamic prediction. Comparing with other graph convolutional networks, the LRGCN reduces redundant operations and introduces skip connections, overcomes the limitations of traditional machine learning methods that require geometric airfoil parameters. By using CFD simulation, a dataset of aerodynamic forces for airfoil profiles was generated, where the nodes and edges of the flow field grids are abstracted to build a graph structure for aerodynamic force prediction. This study takes variations of the airfoil shape and pitch angle into consideration, enabling the prediction of lift and drag to be fast. In the numerical experiments, the LRGCN achieves the result that normalized test set loss is around 10-3 and the R2 is higher than 90%, which exhibits stronger robustness and higher accuracy than three other current convolutional models. And the LRGCN runs 60 times faster than the normal CFD method for the same prediction task, which significantly reduces time costs. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Computational fluid dynamics

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Model-based deep learning for fiber bundle infrared image restoration

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Defence technology（防务技术） 2023年第9期27卷 38-45页

作者： Bo-wen Wang Le Li Hai-bo Yang Jia-xin Chen Yu-hai Li Qian Chen Chao Zuo Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense Nanjing University of Science and TechnologyNanjingJiangsu Province210094China Smart Computational Imaging(SCI)Laboratory Nanjing University of Science and TechnologyNanjingJiangsu Province210094China Science and Technology on Electro-Optical Information Security Control Lab Tianjin300308China

As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual *** to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is *** the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep *** building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated *** results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated *** the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging *** proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.

关键词： Fiber bundle Deep learning Infrared imaging Image restoration

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Real-time litchi detection in complex orchard environments: A portable, low-energy edge computing approach for enhanced automated harvesting

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Artificial Intelligence in Agriculture 2024年第1期11卷 13-22页

作者： Zeyu Jiao Kai Huang Qun Wang Zhenyu Zhong Yingjie Cai Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent ManufacturingGuangdong Academy of SciencesGuangzhouChina School of Economics and Management Beihang UniversityBeijingChina Department of Electronic Engineering The Chinese University of Hong KongHong Kong

Litchi,a succulent and perishable fruit,presents a narrow annual harvest window of under two *** advent of smart agriculture has driven the adoption of visually-guided,automated litchi harvesting ***,conventional approaches typically rely on laboratory-based,high-performance computing equipment,which presents challenges in terms of size,energy consumption,and practical application within litchi *** address these limitations,we propose a real-time litchi detection methodology for complex environments,utilizing portable,low-energy edge computing ***,the litchi orchard imagery is collected to enhance data ***,a convolutional neural network(CNN)-based single-stage detector,YOLOx,is constructed to accurately pinpoint litchi fruit locations within the *** facilitate deployment on portable,low-energy edge devices,we employed channel pruning and layer pruning algorithms to compress the trained model,reducing its size and ***,the knowledge distillation technique is harnessed to fine-tune the *** findings demonstrated that our proposed method achieved a 97.1%compression rate,yielding a compact litchi detection model of a mere 6.9 MB,while maintaining 94.9%average precision and 97.2%average *** 99 frames per second(FPS),the method exhibited a 1.8-fold increase in speed compared to the unprocessed ***,our approach can be readily integrated into portable,low-computational automatic harvesting equipment,ensuring real-time,precise litchi detection within orchard settings.

关键词： Litchi detection Automated harvesting Edge computing Neural networks Model compression

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Phishing detection on Ethereum via transaction subgraphs embedding

IET Blockchain

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IET Blockchain 2023年第4期3卷 194-203页

作者： Lv, Haifeng Ding, Yong Guangxi Key Laboratory of Machine Vision and Intelligent Control WuZhou University Wuzhou China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology WuZhou University Wuzhou China

With the rapid development of blockchain technology in the financial sector, the security of blockchain is being put to the test due to an increase in phishing fraud. Therefore, it is essential to study more effective measures and better solutions. Graph models have been proven to provide abundant information for downstream assignments. In this study, a graph-based embedding classification method is proposed for phishing detection on Ethereum by modeling its transaction records using subgraphs. Initially, the transaction data of normal addresses and an equal number of confirmed phishing addresses are collected through web crawling. Multiple subgraphs using the collected transaction records are constructed, with each subgraph containing a target address and its nearby transaction network. To extract features of the addresses, a modified Graph2Vec model called imgraph2vec is designed, which considers block height, timestamp, and amount of transactions. Finally, the Extreme Gradient Boosting (XGBoost) algorithm is employed to detect phishing and normal addresses. The experimental results show that the proposed method achieves good performance in phishing detection, indicating the effectiveness of imgraph2vec in feature acquisition of transaction networks compared to existing models. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Graphic methods

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Method of Multi-agent Path Planning for Lunar Robots Swarm Based on Improved CBS Algorithm 43

Method of Multi-agent Path Planning for Lunar Robots Swarm B...

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43rd Chinese control Conference, CCC 2024

作者： Feng, Haolong Wu, Songtai Liu, Shengyang Song, Ting Han, Fei Shanghai Aerospace Control Technology Institute Shanghai201109 China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai201109 China Northwestern Polytechnical University Xi'an710072 China

ISBN: (纸本)9789887581581

As lunar missions become increasingly complex, the mission terrain will vary depending on different operating areas. In the future, there may be multiple lunar rovers working together, a more flexible and efficient algorithm to generate mission paths for lunar rover clusters is required. To address the path planning problem of lunar rover groups in large-scale and complex lunar terrain, this paper constructs a gridded map based on lunar image information and the actual lunar surface image is woven into a ring map. Path planning algorithms are modeled on this map, and the actual paths are projected onto the original lunar surface and into missions for implementing multi-lunar rover path planning tasks based on the conflict based search(CBS) algorithm. As the key to CBS algorithm is to resolve conflicts among agents, this paper combines the Deep Q Network with the CBS algorithm to propose a CBS algorithm based on a DQN conflict choice strategy to solve the multi-agent path planning problem for lunar rover clusters. The DQN can learn the policy of an agent, enabling it to better select paths. By integrating DQN with the CBS algorithm, path planning can become more intelligent and efficient. Finally, the simulation verifies that the CBS algorithm can effectively solve the multi-agent path planning problem and the improved CBS algorithm proposed has a certain degree of improvement in terms of solution efficiency. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Lunar missions

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Flow Field Prediction of Variable Geometry using a Novel Deep CNN Model with Dual-Feature of Flow Regions 43

Flow Field Prediction of Variable Geometry using a Novel Dee...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Mai J., Cao Dalian University of Technology Control Science and Engineering Dalian116024 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China

ISBN: (纸本)9789887581581

Flow field prediction of variable geometry aims for the precise prediction of varying flow fields with complex geometric shapes and their variations. By combining a novel deep convolutional neural network (CNN) model with a Computational Fluid Dynamics (CFD) method, the solution of the Navier-Stokes equation can be effectively approximated with a significant reduction in the corresponding computation time cost. This paper proposes an approach named AM UNet-3 for predicting the flow field of variable geometry, which can directly predict the expected flow field of new shapes after the training of deep CNN through extracting and identifying dual features of flow field regions: geometric information features and multi-class channel features. The AM UNet-3 is a convolutional neural network model that combines a U-Net variant structure with three independent decoders and attention mechanisms. The framework of U-Net can learn the complete numerical solutions of the N-S equations to velocity and pressure fields from ground-truth data. The three independent decoders are used to output the pressure field and the velocity components in the x-direction and y-direction respectively. The embedded channel attention and spatial attention mechanisms can enhance the ability of AM UNet-3 to extract fast-changing boundary layer characteristics. The performances of AM UNet-3, U-Net and UNet-3 are tested and compared on a dataset consisting of flow field data with 981 different geometric shapes. Experiment results show that AM UNet-3 gets the fastest inference and the lowest prediction error in the three methods. Compared with the standard CFD method, AM UNet-3 is able to speed up the computation more than three orders of magnitude. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Computational fluid dynamics

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