تعداد نشریات | 38 |
تعداد شمارهها | 1,240 |
تعداد مقالات | 8,994 |
تعداد مشاهده مقاله | 7,844,890 |
تعداد دریافت فایل اصل مقاله | 4,706,539 |
کنترل زمان محدود سیستمهای چندعاملی با روش مود لغزشی تطبیقی در حضور اغتشاش خارجی نامعلوم و شبکه ارتباطی غیر جهتدار | ||
مکانیک هوافضا | ||
مقاله 10، دوره 19، شماره 3 - شماره پیاپی 73، آذر 1402، صفحه 137-148 اصل مقاله (891.69 K) | ||
نوع مقاله: گرایش دینامیک، ارتعاشات و کنترل | ||
نویسنده | ||
حسین چهاردولی* | ||
استادیار، گروه مهندسی مکانیک، دانشکده فنی و مهندسی، دانشگاه آیت ا... بروجردی، بروجرد، ایران | ||
تاریخ دریافت: 12 بهمن 1401، تاریخ بازنگری: 28 فروردین 1402، تاریخ پذیرش: 17 اردیبهشت 1402 | ||
چکیده | ||
در این مقاله، به کنترل زمان محدود سیستمهای چندعاملی مرتبه 2 متشکل از یک رهبر و تعدادی پیرو تحت اغتشاش خارجی پرداخته میشود. شبکه ارتباطی عاملها غیر جهتدار و فاصله بین آنها ثابت در نظر گرفته میشود. هدف، طراحی یک کنترلر مود لغزشی تطبیقی مقاوم است که با تخمین زدن کرانهای بالا و پایین اغتشاش نهتنها پایداری زمان محدود سیستم را تضمین میکند، بلکه عدم افزایش دامنه خطای تعقیب بین عاملها را نیز به همراه خواهد داشت. به این منظور، یک سطح لغزش جدید تعریف میگردد که با صفر شدن آن تحت کنترلر مدنظر، هر دو هدف فوق برآورده میشوند. از قضیه دوم لیاپانوف بهمنظور اثبات پایداری سیستم استفاده میشود و یک تابع لیاپانوف شعاعی نامحدود برحسب سطح لغزش جدید و خطاهای تخمین ارائه میگردد. بر اساس ساختار ارتباطی عاملها، قوانین کنترلی و تطبیقی لازم برای منفی شدن مشتق تابع لیاپانوف به دست خواهند آمد. نتایج حاصله در قالب یک قضیه به همراه اثبات ارائه میشوند. بهمنظور اعتبارسنجی این روش، دو سناریو با حرکات متفاوت رهبر موردبررسی قرار میگیرند. نشان داده میشود که سیستم مزبور تحت روش کنترلی ارائهشده پایدار زمان محدود است و خطای تعقیب بین عاملها به صفر میرسد. | ||
تازه های تحقیق | ||
| ||
کلیدواژهها | ||
سیستم چند عاملی؛ مود لغزشی تطبیقی؛ پایداری زمان محدود؛ اغتشاش خارجی؛ خطای موقعیت | ||
عنوان مقاله [English] | ||
Finite Time Adaptive Sliding Mode Control of Multi-agent Systems via Unknown External Disturbance and Undirected Network | ||
نویسندگان [English] | ||
Hossein Chehardoli | ||
Assistant Professor, Department of Mechanical Engineering, Faculty of Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran | ||
چکیده [English] | ||
In this paper, the finite time control of 2nd order multi-agent systems (MASs) consisting of a leader and a number of followers under external disturbance is discussed. The communication graph of agents is considered non-directional and the distance between them is considered constant. The goal is to design a robust adaptive sliding mode controller which, by estimating the upper and lower limits of the disturbance, not only ensures the finite time stability of the system, but also does not increase the tracking error range between the agents. For this purpose, a new slip surface is defined, which becomes zero under the considered controller, both of the above goals are met. The second Lyapunov theorem is used to prove the stability of the system and an unbounded radial Lyapunov function is presented in terms of the new slip surface and estimation errors. Based on the communication structure of the factors, the necessary control and adaptive rules will be obtained to make the derivative of the Lyapunov function negative. The results are presented in the form of a theorem with proof. In order to validate this method, two scenarios with different movements of the leader are examined. It is shown that the mentioned system is finite time stable under the presented control method and the tracking error between the agents reaches zero. | ||
کلیدواژهها [English] | ||
Multi-agent systems (MAS), Adaptive sliding mode control, Finite time stabilization, External disturbance, Distance error | ||
مراجع | ||
[1] Kawamoto Y, Fadlullah ZM, Nishiyama H, Kato N, Toyoshima M. Prospects and challenges of context-aware multimedia content delivery in cooperative satellite and terrestrial networks. IEEE Communications Magazine. 2014;52(6):55-61.## [2] Goldhoorn A, Garrell A, Alquézar R, Sanfeliu A. Searching and tracking people with cooperative mobile robots. Autonomous Robots. 2018;42(4):739-59.## [3] Fan Y, Hu G, Egerstedt M. Distributed reactive power sharing control for microgrids with event-triggered communication. IEEE Transactions on Control Systems Technology. 2016;25(1):118-28.## [4] Zhang J, Sha J, Han G, Liu J, Qian Y. A cooperative-control-based underwater target escorting mechanism with multiple autonomous underwater vehicles for underwater Internet of Things. IEEE Internet of Things Journal. 2020;8(6):4403-16.## [5] Dorri A, Kanhere SS, Jurdak R. Multi-agent systems: A survey. Ieee Access. 2018;6:28573-93.## [6] Kalech M, Natan A, editors. Model-Based Diagnosis of Multi-Agent Systems: A Survey. Proceedings of the AAAI Conference on Artificial Intelligence; 2022.## [7] Wang Y, Garcia E, Casbeer D, Zhang F. Cooperative control of multi-agent systems: Theory and applications. 2017.## [8] Herrera M, Pérez-Hernández M, Kumar Parlikad A, Izquierdo J. Multi-agent systems and complex networks: Review and applications in systems engineering. Processes. 2020;8(3):312.## [9] Hong Z-W, Su S-Y, Shann T-Y, Chang Y-H, Lee C-Y. A deep policy inference q-network for multi-agent systems. arXiv preprint arXiv:171207893. 2017.## [10] Sun Q, Yao Y, Yi P, Hu Y, Yang Z, Yang G, et al. Learning controlled and targeted communication with the centralized critic for the multi-agent system. Applied Intelligence. 2022:1-19.## [11] Palunko I, Tolić D, Prkačin V. Learning near‐optimal broadcasting intervals in decentralized multi‐agent systems using online least‐square policy iteration. IET Control Theory & Applications. 2021;15(8):1054-67.## [12] Lu K, Xu H, Zheng Y. Distributed resource allocation via multi-agent systems under time-varying networks. Automatica. 2022;136:110059.## [13] Lui DG, Petrillo A, Santini S. An optimal distributed PID-like control for the output containment and leader-following of heterogeneous high-order multi-agent systems. Information Sciences. 2020;541:166-84.## [14] Liao R, Han L, Dong X, Li Q, Ren Z. Finite-time formation-containment tracking for second-order multi-agent systems with a virtual leader of fully unknown input. Neurocomputing. 2020;415:234-46.## [15] Liu P, Xiao F, Wei B, Wang A. Distributed constrained optimization problem of heterogeneous linear multi-agent systems with communication delays. Systems & Control Letters. 2021;155:105002.## [16] Feng X, Yang Y, Wei D. Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems. Neurocomputing. 2021;428:12-8.## [17] Yang Z, Pan X, Zhang Q, Chen Z. Finite-time formation control for first-order multi-agent systems with region constraints. Frontiers of Information Technology & Electronic Engineering. 2021;22(1):134-40.## [18] Zheng Y, Zhao Q, Ma J, Wang L. Second-order consensus of hybrid multi-agent systems. Systems & Control Letters. 2019;125:51-8.## [19] Li S, Nian X, Deng Z. Distributed optimization of general linear multi-agent systems with external disturbance. Journal of the Franklin Institute. 2021;358(11):5951-70.## [20] Dong X, Li Q, Zhao Q, Ren Z. Time‐varying group formation analysis and design for general linear multi‐agent systems with directed topologies. International Journal of Robust and Nonlinear Control. 2017;27(9):1640-52.## [21] Zhang C, Ji L, Yang S, Li H. Optimal antisynchronization control for unknown multiagent systems with deep deterministic policy gradient approach. Information Sciences. 2023;622:946-61.## [22] Peters AA, Middleton RH, Mason O. Leader tracking in homogeneous vehicle platoons with broadcast delays. Automatica. 2014;50(1):64-74.## [23] Dong L, Chai S, Zhang B, Nguang SK. Sliding mode control for multi-agent systems under a time-varying topology. International Journal of Systems Science. 2016;47(9):2193-200.## [24] Khalil HK. Nonlinear control: Pearson New York; 2015.## [25] Zhang J, Lyu M, Shen T, Liu L, Bo Y. Sliding mode control for a class of nonlinear multi-agent system with time delay and uncertainties. IEEE Transactions on Industrial Electronics. 2017;65(1):865-75.## [26] Li W, Niu Y, Cao Z, Lv X. Sliding mode control for multi‐agent systems under stochastic communication protocol. International Journal of Robust and Nonlinear Control. 2022;32(13):7522-35.## [27] Wang J, Luo X, Zhang Y, Guan X. Distributed integrated sliding mode control via neural network and disturbance observer for heterogeneous vehicle systems with uncertainties. Transactions of the Institute of Measurement and Control. 2023:01423312221143655.## [28] Zhao N, Zhu J. Sliding mode control for robust consensus of general linear uncertain multi-agent systems. International Journal of Control, Automation and Systems. 2020;18(8):2170-5.## [29] Manouchehri P, Ghasemi R, Toloei A, Mohammadi F. Distributed neural observer-based formation strategy of non-affine nonlinear multi-agent systems with unknown dynamics. Journal of Circuits, Systems and Computers. 2021;30(5):2130005.## [30] Rahimi N, Binazadeh T. Distributed Adaptive Robust Controller Design for consensus in multi-agent system including robot arms with actuator saturation constraint. Modares Mechanical Engineering. 2019; 19(7):1759-1766.## [31] Kaviri S, Tahsiri A, Taghirad H. A Distributed framework design for formation control of under-actuated USVs in the presence of environmental disturbances using terminal sliding mode control. Journal of Control. 2021;15(1):35-49.## [32] Krstic M, Kokotovic PV, Kanellakopoulos I. Nonlinear and adaptive control design: John Wiley & Sons, Inc.; 1995.## | ||
آمار تعداد مشاهده مقاله: 174 تعداد دریافت فایل اصل مقاله: 188 |