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Parallel Seeds:From Foundation Models to Foundation Intelligence for Agricultural Sustainability

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 481-484页

作者： Laiyi Fu Shunkang Ling Danyang Wu Mengzhen Kang Fei-Yue Wang Hequan Sun the Shool of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong University the College of Information Engineering Northwest A&F University IEEE the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences

THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the development of sustainable agriculture, where a fundamental step is crop breeding to improve agronomic or economic traits, e.g., increasing yields of crops while decreasing resource usage and minimizing pollution to the environment [2].

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Federated Accelerated Deep Reinforcement Learning for Multi-Zone HVAC control in Commercial Buildings

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IEEE Transactions on Smart Grid 2025年第3期16卷 2599-2610页

作者： Xia, Yihan Wang, Xinli Yin, Xiaohong Bo, Wanlin Wang, Lei Li, Shaoyuan Li, Kang Shandong University School of Control Science and Engineering Jinan250100 China Qingdao University of Science and Technology College of Automation and Electronic Engineering Qingdao266101 China University of Leeds School of Electronic and Electric Engineering LeedsLS2 9JT United Kingdom

Deep Reinforcement Learning (DRL) has demonstrated a promising prospect in optimizing Heating, Ventilation, and Air Conditioning (HVAC) systems to minimize energy costs and improve thermal comfort without requiring the knowledge of building thermal dynamic models. However, DRL training can be difficult to converge due to its long exploration, especially in multi-agent scenarios. To address these concerns, we propose a novel Federated Accelerated Multi-Agent DRL (FA-MADRL) algorithm for HVAC control in commercial buildings with multi-zone offices. To be specific, we reformulate the optimal control problem of indoor temperature, CO2 concentration and humidity in multi-zone HVAC as a Markov Decision Process (MDP). Then, we establish a MADRL framework for HVAC system control and utilize a federated learning (FL) mechanism to accelerate the convergence during real-time deployment. Experimental studies have been carried out on a TRNSYS-based commercial building HVAC system with multiple zones. The results demonstrate the superiority of our proposed algorithm, with improved convergence speed, reduced energy consumption, and satisfied thermal comfort. © 2010-2012 IEEE.

关键词： Deep reinforcement learning

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Fully Distributed Model-Free Adaptive Sliding Mode control for MASs With Hybrid-Attacked Topology

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IEEE Transactions on automation Science and engineering 2025年 22卷 12336-12346页

作者： Duan, Shitao Chen, Guangdeng Zhou, Qi Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

A fully distributed model-free adaptive sliding mode control (MFASMC) strategy is proposed in this paper for unknown nonlinear multi-agent systems (MASs), in which topology networks are exposed to hybrid attacks consisting of denial-of-service (DoS) and false data injection attacks. Hybrid attacks in topology networks can result in neighbor information dropouts and inaccuracies among agents. First, the MASs with unknown dynamics are translated to equivalent linear data equations by the dynamic linearization technique. Second, the impact of neighbor information dropouts caused by DoS attacks is mitigated by a designed attack compensation mechanism, in which the compensation error is guaranteed to be bounded in the sense of mathematical expectation. Then, a fully distributed MFASMC algorithm, which does not depend on knowledge of the Laplacian matrix, is designed to improve the robustness of MASs with topology networks exposed to hybrid attacks, thus indirectly mitigating the impact of neighbor information inaccuracies. Finally, the consensus error is rigorously proved to be bounded in the sense of mathematical expectation, and the validity of the proposed strategy is confirmed by simulations. © 2004-2012 IEEE.

关键词： Network topology

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A Fractional-Order Nonlinear Backstepping controller Design for Current-controlled Maglev System

arXiv

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arXiv 2025年

作者： Astekin, Dorukhan Adigüzel, Fatih Yıldız Technical University Electric-Electronic Faculty Department of Control and Automation Engineering İstanbul Turkey

The magnetic levitation system (Maglev) is a nonlinear system by which an object is suspended with no support other than magnetic fields. The main control perspective of the Maglev system is to levitate a steel ball in air by the electromagnetic force. However, the Maglev system has highly nonlinear dynamics which is inconvenient in the sense of sensitive control/regulation of its nonlinear dynamics. In this paper, the nonlinear backstepping controller based on the fractional-order derivative is proposed for the control of the nonlinear current-controlled Maglev system. After, the system dynamics and fractional-order backstepping controller design are given, the asymptotic stability of the closed-loop system is proved by employing the Lyapunov theory. Some computer-based numerical experiments are carried out to show the effectiveness of the proposed controller for the control of Maglev system. © 2025, CC BY.

关键词： Magnetic levitation

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Data-Driven Group Formation control of Cyber-Physical Systems via Distributed Cloud Computing

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 341-350页

作者： Ren, Hongru Long, Yinren Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper investigates the group formation control problem for cyber-physical systems (CPSs) with random communication constraints. The distributed cloud computing system is constructed to divide agents into groups and establish communication between agents. A data-driven predictive control strategy is proposed by combining networked predictive control and model-free adaptive control method. The desired group formation control performance can be achieved and the three-channel random communication constraints of CPSs are actively compensated. Thisstrategy does not require the system model and relies solely on the system's I/O data for adaptive learning. Further analyses concludes the conditions for simultaneous reach stability and group formation of the closed-loop CPSs using the data-driven predictive control strategy. The effectiveness of the proposed strategy is validated by simulation results. © 2023 IEEE.

关键词： Distributed cloud

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Research on twistor-based complex collisions of a quadrotor UAV suspension system

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International Journal of Dynamics and control 2025年第3期13卷 1-14页

作者： Zhou, Jiaxing Chen, Yuhao Deng, Yifan Li, Qing School of Electric Engineering and Automation Xiamen University of Technology 600 Ligong Road Fujian Xiamen361024 China Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control 600 Ligong Road Fujian Xiamen361024 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University No. 28 Xianning West Shaanxi Xi’an710075 China School of Aerospace Northwestern Polytechnical University No. 127 Youyi West Road Xi’an710072 China

The disturbances of wind fields and collisions introduce additional uncertain disturbance during the collision deicing process of UAV suspended load. The UAV suspension system’s position and attitude models are established separately based on traditional method such as Newton–Euler. Consequently, the introduction of multiple algebraic methods, including position vector and Euler angle, increases computational complexity. Accordingly, the system response delays and the control accuracy cannot meet the mission requirements. Therefore, it is essential to find a method for integrated position–attitude modeling and control. To address this problem, this paper proposes a collision dynamic model for a quadrotor UAV suspension system based on twistor. This twistor provides a unified description of translational and rotational motion. In addition, the introduction of twistor avoids the normalization constraints and parameter redundancy in dual quaternion. The stability of the UAV cannot be ensured due to the uncertain disturbances introduced by the wind field and collisions. As a result, there is a need to design a strong disturbance rejection controller. To mitigate impacts of these disturbances, a fuzzy sliding mode controller within the twistor framework is presented. Lyapunov theory and LaSalle invariance principles are used to prove the stability of the system. The simulation demonstrates that the fuzzy sliding mode controller within the twistor framework effectively ensures the stability and adaptability of the quadrotor UAV suspended system under the disturbances of wind fields and collisions. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Disturbance rejection

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Noncooperative Game-Based Composite Voltage Regulation for Microgrid With Switching Topologies and Prescribed Performance

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 1720-1731页

作者： Liu, Yuezhi Chen, Yong Zhang, Longjie Wang, Hongru Hafez, Esam University of Electronic Science and Technology of China School of Automation Engineering Sichuan Chengdu611731 China Sichuan Province Engineering Technology Research Center for Electric Vehicle Driving System and Safety Sichuan Chengdu611731 China College of Electrical and Information Engineering Zhengzhou niversity of Light Industry Zhengzhou450002 China Aswan University Faculty of Energy Engineering Aswan81528 Egypt

In this paper, the voltage regulation issue of the microgrid with the switching topologies, nonlinear coupling and prescribed performance is investigated. Firstly, to remove the requirement of the global topology information, a noncooperative game layer with the fully distributed Nash equilibrium seeking (FDNES) approach is devised. In this study, the noncooperative game is performed over the switching communication topologies. Furthermore, the voltage regulation layer is formulated, which integrates the adaptive coupling observer (ACO) and prescribed performance based composite voltage regulation (PPCVR) methods. Specifically, in the ACO, an adaptive observer gain is devised to attenuate the damaging peak dynamics. For the PPCVR, the prescribed performance function based dynamic surface control strategy is formulated such that the voltage tracking error can be restrained according to the specified constraints, which will enhance the voltage regulation performance. Additionally, the PPCVR utilizes the coupling estimation by the ACO to compensate the nonlinear coupling dynamics. It is shown that the signals of the closed-loop system are semi-global uniformly and ultimately bounded (SGUUB). Finally, comparative studies and hardware-in-loop experiment by RT-LAB platform are performed to illustrate the effectiveness of the proposed voltage regulation strategy. © 2013 IEEE.

关键词： Microgrids

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Deep Learning and Projection Neural Network With Finite-Time Convergence for Energy Management of Multi-Energy System

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IEEE Transactions on Smart Grid 2025年第3期16卷 2156-2168页

作者： Liu, Xueying He, Xing Li, Chaojie Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing102206 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy systems (MES) containing dynamic parameters. First, the sliding mode technique is introduced in the PNN to design a finite-time PNN (FTPNN). The stability and finite-time convergence of the proposed FTPNN are proved by the Lyapunov method and the setting time bound is given. Then, the deep FTPNN (DFTPNN) is designed by combining deep learning with the proposed FTPNN. The dynamic parameters in the MES that change over time are used as input variables for the DFTPNN, allowing the trained DFTPNN to respond immediately to changes in the dynamic parameters and to predict the solutions of the FTPNN with different parameters directly. Simulation experiments show that FTPNN has faster convergence compared to PNN. DFTPNN significantly reduces the computation time compared to FTPNN. DFTPNN provides predicted solutions to FTPNN. Since DFTPNN can respond immediately to changes in dynamic parameters and directly provide energy management strategies under different parameters, it can adapt to changing environments and promote the economic and stable operation of MES. © 2010-2012 IEEE.

关键词： Cogeneration Heuristic algorithms Convergence Deep learning Prediction algorithms Optimization Heating systems Energy management Approximation algorithms Power system stability

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Frequency shift-based 3D shape measurement in the presence of strong inter-reflection

Frequency shift-based 3D shape measurement in the presence o...

引用

2024 International Conference on Optical and Photonic engineering, icOPEN 2024

作者： Miao, Huisi Liu, Wan Yan, Ziyu Xu, Weidong Zhang, Dongbo Zhang, Hui School of Automation and Electronic Information Xiangtan University Xiangtan411105 China Xiangtan Electric Manufacturing Co..Ltd. Xiangtan411105 China National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha410082 China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

Traditional fringe projection profilometry (FPP) struggles with robust imaging and high-precision three-dimensional (3D) reconstruction in complex lighting environments with strong inter-reflection. This paper proposes a method combining frequency-shifted fringe projection and epipolar constraint to achieve high-precision 3D profilometry in such scenarios. First, environmental light interference is mitigated and the maximum fringe frequency is reduced through window function analysis (WFA) and a four-step phase-shifting (PS). Then, direct and indirect illumination are separated in the Fourier domain. Direct illumination positions are identified using threshold segmentation and epipolar geometry constraint, enhancing imaging robustness in strong inter-reflection contexts. Experimental results demonstrate that the method constructs direct and indirect illumination feature models, accurately obtaining direct illumination information and eliminating multi-path interference. Additionally, in tests with complex geometrical surfaces, imaging efficiency and robustness significantly improve, with effective point cloud data increasing by over 30% and planar accuracy improving by 50%. © 2025 SPIE.

关键词： Light interference

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BAT Optimized SMC for Continuous Stirred Tank Reactor

BAT Optimized SMC for Continuous Stirred Tank Reactor

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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Husam E. Elfadel Haroon Congzhi Huang School of Control and Computer Engineering North China Electric Power University Beijing China Department of Electrical and Electronic Engineering Faculty of Engineering Omdurman Islamic University Khartoum Sudan

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

This topic proposed a Sliding Mode control (SMC) strategy utilizing the BAT Algorithm (BAT-SMC) for regulating concentration and temperature in a Continuous Stirred Tank Reactor (CSTR) system. The SMC is designed to maintain concentration (CA) and temperature (T) within desired setpoints by adapting sliding surfaces in the presence of disturbances and system uncertainties. The BAT Algorithm enhances the SMC by optimizing control parameters, reducing chattering effects, and improving tracking accuracy. Simulations validate the efficacy of BAT-SMC, showing improved dynamic performance for both concentration and temperature control compared to other controllers.

关键词： Uncertainty Mechatronics Heuristic algorithms Optimal control Coolants Temperature control Continuous-stirred tank reactor Feeds Information technology Sliding mode control

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