未知大规模互联系统在线分散式动态事件触发控制

何怡睿; 苏涵光; 张化光; 栾鑫洋

doi:10.16383/j.aas.c240262

未知大规模互联系统在线分散式动态事件触发控制

doi: 10.16383/j.aas.c240262 cstr: 32138.14.j.aas.c240262

1.
东北大学信息科学与工程学院沈阳 110819

基金项目: 国家自然科学基金(62373091, 62103087, 62203311, U22A2055), 中国博士后科学基金(2024T170112, 2021M690567), 国家重点研发计划项目(2018YFA0702200), 中央高校基本科研业务费(N2104016, N2304009), 辽宁省自然科学基金联合基金(2023-MSBA-082), 辽宁省综合能源优化与安全运行重点实验室, 中国工程院院地合作咨询项目(2023-DFZD-60, 2023-DFZD-60-03)资助

详细信息

作者简介:
何怡睿：东北大学信息科学与工程学院本科生. 主要研究方向为大规模系统的最优控制, 事件触发, 自适应动态规划. E-mail: 20212366@stu.neu.edu.cn

苏涵光：东北大学信息科学与工程学院副教授. 主要研究方向为综合能源系统及其优化控制, 自适应动态规划, 人工智能技术. 本文通信作者. E-mail: suhanguang@ise.neu.edu.cn

张化光：东北大学教授, 教育部长江学者, IEEE Fellow. 主要研究方向为自适应动态规划, 模糊控制, 网络控制, 混沌控制, 能源互联网. E-mail: hgzhang@ieee.org

栾鑫洋：东北大学信息科学与工程学院博士研究生. 2024年获东北大学硕士学位. 主要研究方向为大规模系统的最优控制, 综合能源系统, 自适应动态规划. E-mail: luanxinyang_daria@163.com

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- 文章访问数: 39
- HTML全文浏览量: 27
- 被引次数: 0
出版历程
- 收稿日期: 2024-05-13
- 录用日期: 2024-08-27
- 网络出版日期: 2024-11-07

Online Decentralized Dynamic Event-triggered Control of Unknown Large-scale Interconnected Systems

1.
College of Information Science and Engineering, Northeast ern University, Shenyang 110819

Funds: Supported by National Natural Science Foundation of China (62373091, 62103087, 62203311, U22A2055), China Postdoctoral Science Foundation (2024T170112, 2021M690567), National Key R & D Program of China (2018YFA0702200), the Fundamental Research Funds for the Central Universities (N2104016, N2304009), Natural Science Foundation of Liaoning Province (2023-MSBA-082), Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, and China Academy of Engineering institute of Land Cooperation Consulting Project (2023-DFZD-60, 2023-DFZD-60-03)

More Information

Author Bio:
HE Yi-Rui　Undergraduate student at the School of Information Science and Engineering, Northeastern University. His research interest covers optimal control of large-scale systems event trigger and adaptive dynamic programming

SU Han-Guang　Associate professor at the School of Information Science and Engineering, Northeastern University. His research interest covers integrated energy systems and their optimization control, adaptive dynamic programming, and artificial intelligence technologies. Corresponding author of this paper

ZHANG Hua-Guang　Professor at the School of Information Science and Engineering, Northeastern University. Changjiang Scholars of Ministry of Education and IEEE Fellow. His research interest covers adaptive dynamic programming, fuzzy control, network control, chaos control and energy internet

LUAN Xin-Yang　Ph.D. candidate at the School of Information Science and Engineering, Northeastern University. She received master degree from Northeastern University in 2024. Her research interest covers optimal control of large-scale systems, integrated energy systems, and adaptive dynamic programming

摘要

摘要: 针对一类系统动态未知且受互联项影响的非线性互联大规模系统, 提出一种新的在线分散式动态事件触发控制(Dynamic event-triggered control, DETC)方案. 首先, 构建基于神经网络的辨识器来重构互联系统的未知内部动态. 其次, 使用自适应评判网络在事件触发机制下学习近似最优控制策略. 在所设计的动态事件触发控制机制下, 各子系统独立地设计自己的控制策略, 且各控制策略的更新是异步进行的. 也就是说, 各个分散式事件触发条件和控制器仅依赖于各自子系统的局部状态信息, 而无需频繁获取相邻子系统的信息, 从而规避通过通信网络在子系统间传递状态信息的需求. 然后, 借助李雅普诺夫稳定性定理, 从理论上证明所提出的闭环控制系统状态和评判网络权值估计误差都是最终一致有界的. 最后, 通过一个数值仿真示例和一个实际工程示例验证所提出的动态事件触发控制方法的有效性和实用性.
- 自适应动态规划 /
- 动态事件触发控制 /
- 大规模系统 /
- 控制受限
Abstract: This article introduces a novel online decentralized dynamic event-triggered control (DETC) scheme designed for a class of nonlinear interconnected large-scale systems whose system dynamics are unknown and influenced by interconnections. Firstly, we utilize a neural network-based identifier to reconstruct these unknown internal dynamics. Secondly, we develop an adaptive critic network to learn the approximate optimal control policies under a dynamic event-triggered mechanism. In this mechanism, the control policies for each subsystem are designed independently, and the update of the control policies is completed asynchronously. In other words, each decentralized event trigger condition and controller only rely on the local state information of their respective subsystems, and there is no need to frequently obtain information from adjacent subsystems, thus circumventing the need to transfer state information between subsystems through communication networks. Furthermore, by employing Lyapunov theorem, we demonstrate that both the closed-loop control system states and the critic network weights estimation errors are ultimately uniformly bounded. Finally, the effectiveness and feasibility of the developed dynamic event-triggered control method are validated through both a numerical example and a real-world application.
- Adaptive dynamic programming (ADP) /
- dynamic event-triggered control (DETC) /
- large-scale systems /
- control constraint

HTML全文

图 1 三阶互联系统(69)控制过程中

Fig. 1 For the three-order interconnected systems (69)

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图 2 三阶互联系统(69)控制过程中

Fig. 2 For the three-order interconnected systems (69)

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图 3 三阶互联系统(69)控制过程中

Fig. 3 For the three-order interconnected systems (69)

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图 4 三阶互联系统(69)子系统1、2和3的触发时刻

Fig. 4 For the three-order interconnected systems (69), the triggering instants for subsystems 1, 2 and 3

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图 5 三机互联电力系统(70)控制过程中

Fig. 5 For the three-machine power interconnected systems (70)

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图 6 三机互联电力系统(70)控制过程中

Fig. 6 For the three-machine power interconnected systems (70)

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图 7 三机互联电力系统(70)控制过程中

Fig. 7 For the three-machine power interconnected systems (70)

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图 8 三机互联电力系统(70)子系统1、2和3的触发时刻

Fig. 8 For the three-machine power interconnected systems (70), the triggering instants for subsystems 1, 2 and 3

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参考文献(49)

[1]	葛泉波, 王远亮, 李宏. 基于改进舰尾流模型和多层耦合分析的机载雷达测量建模. 自动化学报, 2024, 50(3): 617−639 Ge Quan-Bo, Wang Yuan-Liang, Li Hong. Airborne radar measurement modeling based on improved carrier air wake model and multi-layer coupling analysis. Acta Automatica Sinica, 2024, 50(3): 617−639
[2]	Zhang Y Y, Huang Y, Huang C, Huang H L, Nguyen A T. Joint optimization of deployment and flight planning of multi-UAVs for long-distance data collection from large-scale IoT devices. IEEE Internet of Things Journal, 2024, 11(1): 791−804 doi: 10.1109/JIOT.2023.3285942
[3]	Liu Z, Wei H S, Wang H Y, Li H A, Wang H S. Integrated task allocation and path coordination for large-scale robot networks with uncertainties. IEEE Transactions on Automation Science and Engineering, 2022, 19(4): 2750−2761 doi: 10.1109/TASE.2021.3111888
[4]	Zhang H, Chen X Y L, Lu H M, Xiao J H. Distributed and collaborative monocular simultaneous localization and mapping for multi-robot systems in large-scale environments. International Journal of Advanced Robotic Systems, 2018, 15(3): 1−20
[5]	Devlin-Hill B, Calinescu R, Cámara J, Caliskanelli I. Towards scalable multi-robot systems by partitioning the task domain. In: Proceedings of the 23rd Annual Conference on Towards Autonomous Robotic Systems. Culham, UK: Springer, 2022. 282−292
[6]	王本斐, 张荣辉, 冯国栋, Ujjal M, 郭戈. 基于事件触发的直流微电网无差拍预测控制. 自动化学报, 2024, 50(3): 475−485 Wang Ben-Fei, Zhang Rong-Hui, Feng Guo-Dong, Ujjal M, Guo Ge. Event-triggered deadbeat predictive control for DC microgrid. Acta Automatica Sinica, 2024, 50(3): 475−485
[7]	王睿, 孙秋野, 张化光. 信息能源系统的信物融合稳定性分析. 自动化学报, 2023, 49(2): 307−316 Wang Rui, Sun Qiu-Ye, Zhang Hua-Guang. Stability analysis of cyber-physical fusion in cyber-energy systems. Acta Automatica Sinica, 2023, 49(2): 307−316
[8]	王澄, 刘德荣, 魏庆来, 赵冬斌, 夏振超. 带有储能设备的智能电网电能迭代自适应动态规划最优控制. 自动化学报, 2014, 40(9): 1984−1990 Wang Cheng, Liu De-Rong, Wei Qing-Lai, Zhao Dong-Bin, Xia Zhen-Chao. Iterative adaptive dynamic programming approach to power optimal control for smart grid with energy storage devices. Acta Automatica Sinica, 2014, 40(9): 1984−1990
[9]	Byun H. Learning-based adaptive feedback control for tracking optimisation in wireless sensor actuator networking systems. IET Communications, 2022, 16(3): 218−226 doi: 10.1049/cmu2.12332
[10]	Chen C H, Lin M Y, Tew W P. Wireless fieldbus networking with precision time synchronization for a low-power WSAN. Microprocessors and Microsystems, 2022, 90: Article No. 104509 doi: 10.1016/j.micpro.2022.104509
[11]	Park H S, Moon S, Kwak J, Park K J. CAPL: Criticality-aware adaptive path learning for industrial wireless sensor-actuator networks. IEEE Transactions on Industrial Informatics, 2023, 19(8): 9123−9133 doi: 10.1109/TII.2022.3217471
[12]	Vu V P. A polynomial decentralized controller design for a large-scale nonlinear system: SOS approach. IEEE Access, 2022, 10: 44008−44022 doi: 10.1109/ACCESS.2022.3169898
[13]	Sarbaz M, Zamani I, Manthouri M, Ibeas A. Decentralized robust interval type-2 fuzzy model predictive control for Takagi-Sugeno large-scale systems. Automatika, 2022, 63(1): 49−63 doi: 10.1080/00051144.2021.2003113
[14]	Sun Y F, Mao Y, Yu H S, Liu H B. (Q, S, R)-Dissipativity analysis of large-scale networked systems. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 2023, 70(12): 4424−4428 doi: 10.1109/TCSII.2023.3282972
[15]	Zhang J, Li S, Ahn C K, Xiang Z R. Decentralized event-triggered adaptive fuzzy control for nonlinear switched large-scale systems with input delay via command-filtered backstepping. IEEE Transactions on Fuzzy Systems, 2022, 30(6): 2118−2123 doi: 10.1109/TFUZZ.2021.3066297
[16]	Zhang S C, Zhao B, Liu D R, Zhang Y W. Event-triggered decentralized integral sliding mode control for input-constrained nonlinear large-scale systems with actuator failures. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024, 54(3): 1914−1925 doi: 10.1109/TSMC.2023.3331150
[17]	Wei J, Wu Q W, Li C B, Huang S, Zhou B, Chen D W. Hierarchical event-triggered MPC-based coordinated control for HVRT and voltage restoration of large-scale wind farm. IEEE Transactions on Sustainable Energy, 2022, 13(3): 1819−1829 doi: 10.1109/TSTE.2022.3172933
[18]	Wu Z Y, Zhang A B, Yu T, Li Y M, Xiong J L, Xie M. Dynamic probability-density-dependent event-triggered L_∞ LFC for power systems subject to stochastic delays. IEEE Transactions on Network Science and Engineering, 2024, 11(1): 453−462 doi: 10.1109/TNSE.2023.3300876
[19]	Zhang J, Zhang H G, Gao Z Y, Sun S X. Time-varying formation control with general linear multi-agent systems by distributed event-triggered mechanisms under fixed and switching topologies. Neural Computing and Applications, 2022, 34(6): 4277−4294 doi: 10.1007/s00521-021-06539-w
[20]	Zhao F Y, Gao W N, Liu T F, Jiang Z P. Event-triggered robust adaptive dynamic programming with output feedback for large-scale systems. IEEE Transactions on Control of Network Systems, 2023, 10(1): 63−74 doi: 10.1109/TCNS.2022.3186623
[21]	Bi W S, Wang T. Adaptive fuzzy decentralized control for nonstrict feedback nonlinear systems with unmodeled dynamics. IEEE Transactions on Systems, Man, and Cybernetic: Systems, 2022, 52(1): 275−286 doi: 10.1109/TSMC.2020.2997703
[22]	Gao Z F, Shen K H, Sha X Q, He J Q. Decentralized adaptive PI fault tolerant tracking control for strong interconnected nonlinear systems subject to unmodeled dynamics and actuator faults. Nonlinear Analysis: Hybrid Systems, 2023, 50: Article No. 101394 doi: 10.1016/j.nahs.2023.101394
[23]	Zhang T P, Tang H L, Xia X N, Yi Y. Decentralized adaptive output feedback dynamic surface control for stochastic nonstrict-feedback interconnected nonlinear systems with actuator failures and input quantization via command filter. International Journal of Robust and Nonlinear Control, 2022, 32(12): 6739−6766 doi: 10.1002/rnc.6165
[24]	Cheng Y, Niu B, Zhao X D, Zong G D, Ahmad A M. Event-triggered adaptive decentralised control of interconnected nonlinear systems with Bouc-Wen hysteresis input. International Journal of Systems Science, 2023, 54(6): 1275−1288 doi: 10.1080/00207721.2023.2169845
[25]	Su H G, Zhang H G, Liang X D, Liu C. Decentralized event-triggered online adaptive control of unknown large-scale systems over wireless communication networks. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(11): 4907−4919 doi: 10.1109/TNNLS.2019.2959005
[26]	王鼎, 穆朝絮, 刘德荣. 基于迭代神经动态规划的数据驱动非线性近似最优调节. 自动化学报, 2017, 43(3): 366−375 Wang Ding, Mu Chao-Xu, Liu De-Rong. Data-driven nonlinear near-optimal regulation based on iterative neural dynamic programming. Acta Automatica Sinica, 2017, 43(3): 366−375
[27]	Wang D, Fan W Q, Li M H, Qiao J F. Decentralised tracking control based on critic learning for nonlinear disturbed interconnected systems. International Journal of Systems Science, 2023, 54(5): 1150−1164 doi: 10.1080/00207721.2023.2168143
[28]	Zhang Q C, Zhao D B, Zhu Y H. Event-triggered H_∞ control for continuous-time nonlinear system via concurrent learning. IEEE Transactions on Systems Man, and Cybernetics: Systems, 2017, 47(7): 1071−1081 doi: 10.1109/TSMC.2016.2531680
[29]	Zhao B, Wang D, Shi G, Liu D R, Li Y C. Decentralized control for large-scale nonlinear systems with unknown mismatched interconnections via policy iteration. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48(10): 1725−1735 doi: 10.1109/TSMC.2017.2690665
[30]	Li Y, Zhang H, Wang Z P, Huang C, Yan H C. Data-driven decentralized control for large-scale systems with sparsity and communication delays. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53(9): 5614−5624 doi: 10.1109/TSMC.2023.3274292
[31]	Hu C H, Zou Y Y, Li S Y. Adaptive dynamic programming-based decentralized event-triggered control of large-scale nonlinear systems. Asian Journal of Control, 2022, 24(4): 1542−1556 doi: 10.1002/asjc.2662
[32]	Wang Y J, Wang T, Yang X B, Yang J E. Decentralized optimal tracking control for large-scale nonlinear systems with tracking error constraints. International Journal of Adaptive Control and Signal Processing, 2021, 35(7): 1388−1403 doi: 10.1002/acs.3248
[33]	Tan L N, Tran H T, Tran T T. Event-triggered observers and distributed H_∞ control of physically interconnected nonholonomic mechanical agents in harsh conditions. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(12): 7871−7884 doi: 10.1109/TSMC.2022.3177043
[34]	Tan L N, Gupta N, Derawi M. Adaptive dynamic programming and zero-sum game-based distributed control for energy management systems with internet of things. IEEE Internet of Things Journal, 2023, 10(24): 22371−22385 doi: 10.1109/JIOT.2023.3303448
[35]	罗彪, 欧阳志华, 易昕宁, 刘德荣. 基于自适应动态规划的移动机器人视觉伺服跟踪控制. 自动化学报, 2023, 49(11): 2286−2296 Luo Biao, Ouyang Zhi-Hua, Yi Xin-Ning, Liu De-Rong. Adaptive dynamic programming based visual servoing tracking control for mobile robots. Acta Automatica Sinica, 2023, 49(11): 2286−2296
[36]	Wei Q L, Liu D R, Liu Y, Song R Z. Optimal constrained self-learning battery sequential management in microgrid via adaptive dynamic programming. IEEE/CAA Journal of Automatica Sinica, 2017, 4(2): 168−176 doi: 10.1109/JAS.2016.7510262
[37]	Zhang J C, Zhao X W, Wei X. Reinforcement learning-based structural control of floating wind turbines. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(3): 1603−1613 doi: 10.1109/TSMC.2020.3032622
[38]	Wang Z Y, Wei Q L, Liu D R. A tracking control method based on event-triggered adaptive dynamic programming. In: Proceedings of the 38th Chinese Control Conference. Guangzhou, China: IEEE, 2019. 2454-2459
[39]	Peng Z A, Zhang Z Q, Luo R, Kuang Y Q, Hu J P, Cheng H, et al. Event-triggered optimal control of completely unknown nonlinear systems via identifier-critic learning. Kybernetika, 2023, 59(3): 365−391
[40]	Wang D, Mu C X, Liu D R, Ma H W. On mixed data and event driven design for adaptive-critic-based nonlinear H_∞ control. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(4): 993−1005 doi: 10.1109/TNNLS.2016.2642128
[41]	Dahal R, Kar I. Event-triggered adaptive dynamic programming based guaranteed cost tracking controller for uncertain nonlinear systems. In: Proceedings of the 8th Indian Control Conference (ICC). Chennai, India: IEEE, 2022. 206−211
[42]	Zhong X N, He H B. An event-triggered ADP control approach for continuous-time system with unknown internal states. IEEE Transactions on Cybernetics, 2017, 47(3): 683−694 doi: 10.1109/TCYB.2016.2523878
[43]	Vamvoudakis K G, Lewis F L. Online actor critic algorithm to solve the continuous-time infinite horizon optimal control problem. In: Proceedings of the International Joint Conference on Neural Networks. Atlanta, USA: IEEE, 2009. 3180−3187
[44]	Su H G, Zhang H G, Jiang H, Wen Y L. Decentralized event-triggered adaptive control of discrete-time nonzero-sum games over wireless sensor-actuator networks with input constraints. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(10): 4254−4266 doi: 10.1109/TNNLS.2019.2953613
[45]	Su H G, Luan X Y, Zhang H G, Liang X D, Yang J Z, Wang J W. Decentralized optimal control of large-scale partially unknown nonlinear mismatched interconnected systems based on dynamic event-triggered control. Neurocomputing, 2024, 568: 127013 doi: 10.1016/j.neucom.2023.127013
[46]	Sahoo A, Xu H, Jagannathan S. Near optimal event-triggered control of nonlinear discrete-time systems using neurodynamic programming. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(9): 1801−1815 doi: 10.1109/TNNLS.2015.2453320
[47]	Vamvoudakis K G. Event-triggered optimal adaptive control algorithm for continuous-time nonlinear systems. IEEE/CAA Journal of Automatica Sinica, 2014, 1(3): 282−293 doi: 10.1109/JAS.2014.7004686
[48]	Vamvoudakis K G. An online actor/critic algorithm for event-triggered optimal control of continuous-time nonlinear systems. In: Proceedings of the American Control Conference. Portland, USA: IEEE, 2014. 1−6.
[49]	Wang D, Mu C X, He H B, Liu D R. Event-driven adaptive robust control of nonlinear systems with uncertainties through NDP strategy. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47(7): 1358−1370 doi: 10.1109/TSMC.2016.2592682