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An event-triggered adaptive control algorithm is proposed for cooperative tracking control of high-order nonlinear multiagent systems (MASs) with prescribed performance and full-state constraints. The algorithm combines dynamic surface technology and the backstepping recursive design method, with radial basis function neural networks (RBFNNs) used to approximate the unknown nonlinearity. The barrier Lyapunov function and finite-time stability theory are employed to prove that all agent states are semi-globally uniform and ultimately bounded, with the tracking error converging to a bounded neighborhood of zero in a finite time. Numerical simulations are provided to demonstrate the effectiveness of the proposed control scheme.
Rocznik
Tom
Strony
439--448
Opis fizyczny
Bibliogr. 30 poz., rys., wykr.
Twórcy
autor
- College of Railway Electrification and Electrical Engineering, Hunan Vocational College of Railway Technology, No. 1, Zhihui Road, Shifeng District, Zhuzhou 412006, China
autor
- College of Information and Intelligent Transportation, Fujian Chuanzheng Communications College, No. 80, Shoushan Road, Cangshan District, Fuzhou 350007, China
autor
- College of Information and Intelligent Transportation, Fujian Chuanzheng Communications College, No. 80, Shoushan Road, Cangshan District, Fuzhou 350007, China
autor
- Institute of Microelectronics, University of Macau, Daxue Dama Road, Taipa 999078, Macau, China
Bibliografia
- [1] Chen, L., Mei, J., Li, C. and Ma, G. (2020). Distributed leader-follower affine formation maneuver control for high-order multiagent systems, IEEE Transactions on Automatic Control 65(11): 4941-4948.
- [2] Defoort, M., Polyakov, A., Demesure, G., Djemai, M. and Veluvolu, K. (2015). Leader-follower fixed-time consensus for multi-agent systems with unknown non-linear inherent dynamics, IET Control Theory & Applications 9(14): 2165-2170.
- [3] El-Ferik, S., Hashim, H.A. and Lewis, F.L. (2018). Neuro-adaptive distributed control with prescribed performance for the synchronization of unknown nonlinear networked systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems 48(12): 2135-2144.
- [4] Farrera, B., López-Estrada, F.-R., Chadli, M., Valencia-Palomo, G. and Gómez-Peñate, S. (2020). Distributed fault estimation of multi-agent systems using a proportional-integral observer: A leader-following application, International Journal of Applied Mathematics and Computer Science 30(3): 551-560, DOI: 10.34768/amcs-2020-0040.
- [5] Girard, A. (2015). Dynamic triggering mechanisms for event-triggered control, IEEE Transactions on Automatic Control 60(7): 1992-1997.
- [6] Girosi, F. and Poggio, T. (1990). Networks and the best approximation property, Biological Cybernetics 63: 169-176.
- [7] Henry, Y.W. (1912). On classes of summable functions and their Fourier series, Proceedings of the Royal Society A 87(594): 225-229.
- [8] Hong, H., Yu, W., Wen, G. and Yu, X. (2017). Distributed robust fixed-time consensus for nonlinear and disturbed multi-agent systems, IEEE Transactions on Systems Man & Cybernectis: Systems 47(7): 1464-1473.
- [9] Huang, J. and Wang, Q.-G. (2019). Event-triggered adaptive control of a class of nonlinear systems, ISA Transactions 94: 10-16.
- [10] Huang, N., Duan, Z., Wen, G. and Zhao, Y. (2016). Event-triggered consensus tracking of multi-agent systems with Lur’e nonlinear dynamics, International Journal of Control 89(5): 1-23.
- [11] Hui, Q., Haddad, W.M. and Bhat, S.P. (2008). Finite-time semistability and consensus for nonlinear dynamical networks, IEEE Transactions on Automatic Control 53(8): 1887-1900.
- [12] Li, J., Zhang, A. and Peng, C. (2023). Neuro-adaptive cooperative control for a class of high-order nonlinear multi-agent systems, Measurement and Control 56(5-6): 928-937.
- [13] Li, S.H., Du, H.B. and Lin, X.Z. (2011). Finite-time consensus algorithm for multi-agent systems with double-integrator dynamics, Automatica 47(8): 1706-1712.
- [14] Li, T.-S., Wang, D., Feng, G. and Tong, S.-C. (2010). A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems, IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics 40(3): 915-927.
- [15] Liu, J., Zhang, Y., Yu, Y. and Sun, C. (2020). Fixed-time leader-follower consensus of networked nonlinear systems via event/self-triggered control, IEEE Transactions on Neural Networks and Learning Systems 31(11): 5029-5037.
- [16] Ni, W. and Cheng, D. (2010). Leader-following consensus of multi-agent systems under fixed and switching topologies, Systems and Control Letters 59(3): 209-217.
- [17] Peng, C., Zhang, A. and Li, J. (2021). Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties, International Journal of Applied Mathematics and Computer Science 31(4): 635-645, DOI: 10.34768/amcs-2021-0044.
- [18] Polyakov, A. (2012). Nonlinear feedback design for fixed-time stabilization of linear control systems, IEEE Transactions on Automatic Control 57(8): 2106-2110.
- [19] Ren, B., Ge, S.S., Tee, K.P. and Lee, T.H. (2010). Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function, IEEE Transactions on Neural Networks 21(8): 1339-1345.
- [20] Wang, L. and Feng, X. (2010). Finite-time consensus problems for networks of dynamic agents, IEEE Transactions on Automatic Control 55(4): 950-955.
- [21] Wang, Q., Wang, Y. and Sun, C. (2016). Fixed-time consensus of multi-agent systems with directed and intermittent communications, Asian Journal of Control 19(1): 95-105.
- [22] Wu, Y., Gou, J., Hu, X. and Huang, Y. (2020). A new consensus theory-based method for formation control and obstacle avoidance of UAVs, Aerospace Science and Technology 107: 106332.
- [23] Yang, N. and Li, J. (2020). Distributed robust adaptive learning coordination control for high-order nonlinear multi-agent systems with input saturation, IEEE Access 8: 9953-9964.
- [24] Yang, X., Liao, L., Yang, Q., Sun, B. and Xi, J. (2021). Limited-energy output formation for multiagent systems with intermittent interactions, Journal of the Franklin Institute 358(13): 6462-6489.
- [25] Zegers, F.M., Deptula, P., Shea, J.M. and Dixon, W.E. (2022). Event/self-triggered approximate leader-follower consensus with resilience to byzantine adversaries, IEEE Transactions on Automatic Control 67(3): 1356-1370.
- [26] Zhang, A., Zhou, Y., Chen, Q. and Gong, S. (2018). Adaptive cooperative tracking control for a class of high-order nonaffine nonlinear multi-agent systems, Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018, Shenyang, China, pp. 801-807.
- [27] Zhang, H., Yue, D., Yin, X., Hu, S. and Dou, C.x. (2016). Finite-time distributed event-triggered consensus control for multi-agent systems, Information Sciences 339: 132-142.
- [28] Zhou, H., Sui, S. and Tong, S. (2022). Finite-time adaptive fuzzy prescribed performance formation control for high-order nonlinear multi-agent systems based on event-triggered mechanism, IEEE Transactions on Fuzzy Systems 31(4): 1229-1239.
- [29] Zhou, J., Hu, Q. and Friswell, M.I. (2013). Decentralized finite time attitude synchronization control of satellite formation flying, Journal of Guidance Control and Dynamics 36(1): 185-195.
- [30] Zhu, Y., Guan, X., Luo, X. and Li, S. (2015). Finite-time consensus of multi-agent system via nonlinear event-triggered control strategy, IET Control Theory & Applications 9(17): 2548-2552.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-207d5665-6d5c-4070-88ca-952916e42788