Fixed-Time Bipartite Formation Tracking Control for Multi-USV Systems with Uncertain Model Parameters
-
摘要: 针对模型参数不确定下多无人艇系统的固定时间二分编队跟踪控制问题,通过将命令滤波与复合学习技术融合到反推控制方法中, 提出了一种新型分布式固定时间二分编队跟踪控制协议.首先, 将命令滤波引入到反推控制中, 进而分别设计了虚拟控制协议与真实控制协议.在此基础上, 为估计未知参数设计了参数复合学习律, 利用在线记录的数据和即时数据来产生预测误差, 并利用跟踪误差和预测误差来更新参数估计.结果表明, 在严格弱于持续激励条件的区间激励条件下, 本文提出的控制方案不仅能够保证编队误差的固定时间收敛性也能够保证参数估计误差的固定时间收敛性, 同时解决了多无人艇系统的固定时间二分编队跟踪控制问题. 最后, 通过仿真实验验证了本文提出的控制协议的有效性.Abstract: By integrating command filtering and composite learning techniques with a backstepping control approach, this paper proposes a new class of distributed fixed-time bipartite formation tracking control protocols to address the fixed-time bipartite formation tracking problem for multiple unmanned surface vehicles (USVs) under parametric uncertainties. Firstly, the command filtering technique is introduced into the backstepping control method where the virtual and real control policies are designed respectively. On this basis, composite learning laws are constructed to estimate unknown model parameters. Specifically, prediction errors are generated using both online recorded data and real-time data, and parameter estimates are updated utilizing both tracking and prediction errors. The results demonstrate that under interval excitation conditions, which are less stringent than persistent excitation conditions, the proposed control scheme ensures fixed-time convergence of both formation errors and parameter estimation errors, effectively solving the fixed-time bipartite formation tracking control problem for multi-USV. Finally, the effectiveness of the present control scheme is demonstrated by performing simulation experiments.
-
图 3 参数估计误差 $\tilde{\eta}_{0i}(t),\; i=1,\;2,\;\cdots,\;6$
Fig. 3 Parameter estimation errors $\tilde{\eta}_{0i}(t),\; i=1,\;2,\;\cdots,\;6$
图 4 参数估计误差$\tilde{\Theta}_{i}(t),\; i=1,\;2,\;\cdots,\;6$
Fig. 4 Parameter estimation errors $\tilde{\Theta}_{i}(t),\; i=1,\;2,\;\cdots,\;6$
图 5 局部跟踪误差$\epsilon_{i1}(t),\; \epsilon_{i2}(t),\; i=1,\;2,\;\cdots,\;6$
Fig. 5 Local tracking errors $\epsilon_{i1}(t),\;$ $\epsilon_{i2}(t),\;$ $i=1,\;2,\;\cdots,\;6$
-
[1] Dong Z, Zhang Z, Qi S, Zhang H, Li J, and Liu, Y. Autonomous cooperative formation control of underactuated USVs based on improved MPC in complex ocean environment. Ocean Engineering, 2023, 270: 113633 doi: 10.1016/j.oceaneng.2023.113633 [2] 王端松, 李东禹, 梁晓玲. 干扰条件下无人艇编队有限时间同步控制. 自动化学报, 2024, 50(5): 1047−1058Wang D, Li D, and Liang X. Finite time synchronized formation control of unmanned surface vehicles with external disturbances. Acta Automatica Sinica, 2024, 50(5): 1047−1058 [3] Wen G, Lam J, Fu J, and Wang S. Distributed MPC-based robust collision avoidance formation navigation of constrained multiple USVs. IEEE Transactions on Intelligent Vehicles, 2023, 9(1): 1804−1816 [4] 王宁, 刘永金, 高颖. 未知扰动下的无人艇编队优化轨迹跟踪控制. 中国舰船研究, 2024, 19(1): 178−190Wang N, Liu Y, and Gao Y. Optimal trajectory tracking control of unmanned surface vehicle formation under unknown disturbances. Chinese Journal of Ship Research, 2024, 19(1): 178−190 [5] 王宁, 高颖, 王仁慧. 状态测量不确定和动力学未知的无人艇固定时间容错控制. 自动化学报, 2023, 49(5): 1050−1061Wang N, Gao Y, and Wang R. Fixed-time fault-tolerance control of an unmanned surface vehicle with uncertain measurements and unknown dynamics. Acta Automatica Sinica, 2023, 49(5): 1050−1061 [6] Wang C, Wang Y, Han Q, and Xie W. Multi-USV cooperative formation control via deep reinforcement learning with deceleration. IEEE Transactions on Intelligent Vehicles, doi: 10.1109/TIV.2024.3437735 [7] Pan J, Han T, Xiao B, and Yan H. Predefined-time bipartite time-varying formation tracking control of networked autonomous surface vehicles via hierarchical control approach. IEEE Transactions on Vehicular Technology, 2024, 73(7): 9536−9545 doi: 10.1109/TVT.2024.3364364 [8] Wen G. On distributed leader escort control and bipartite time-varying formation tracking of multiple ASVs. In: 2022 IEEE International Conference on Unmanned Systems (ICUS). Guangzhou, China, 2022: 522-528. [9] Zhou W, Wang Y, Ahn C K, Cheng J, and Chen C. Adaptive fuzzy backstepping-based formation control of unmanned surface vehicles with unknown model nonlinearity and actuator saturation. IEEE Transactions on Vehicular Technology, 2020, 69(12): 14749−14764 doi: 10.1109/TVT.2020.3039220 [10] Peng Z, Wang D, Li T, and Han M. Output-feedback cooperative formation maneuvering of autonomous surface vehicles with connectivity preservation and collision avoidance. IEEE Transactions on Cybernetics, 2019, 50(6): 2527−2535 [11] Peng Z, Wang D, and Hu X. Robust adaptive formation control of underactuated autonomous surface vehicles with uncertain dynamics. IET Control Theory & Applications, 2011, 5(12): 1378−1387 [12] Rout R and Subudhi B. A backstepping approach for the formation control of multiple autonomous underwater vehicles using a leader-follower strategy. Journal of Marine Engineering & Technology, 2016, 15(1): 38−46 [13] Kwan C and Lewis F L. Robust backstepping control of nonlinear systems using neural networks. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2000, 30(6): 753−766 doi: 10.1109/3468.895898 [14] Zhou J and Wen C. Adaptive backstepping control of uncertain systems. Heidelberg, Germany: Springer, 2008. [15] Li N, Liu X, Liu C, He W, and Wang H. Adaptive stabilization control for a class of non-strict feedback underactuated nonlinear systems by backstepping. IEEE Transactions on Automation Science and Engineering, doi: 10.1109/TASE.2024.3392877 [16] Farrell J A, Polycarpou M, Sharma M, and Dong W. Command filtered backstepping. IEEE Transactions on Automatic Control, 2009, 54(6): 1391−1395 doi: 10.1109/TAC.2009.2015562 [17] Ghommam J, Saad M, Mnif F, and Zhu Q. Guaranteed performance design for formation tracking and collision avoidance of multiple USVs with disturbances and unmodeled dynamics. IEEE Systems Journal, 2020, 15(3): 4346−4357 [18] Gonzalez-Garcia A and Castañeda H. Guidance and control based on adaptive sliding mode strategy for a USV subject to uncertainties. IEEE Journal of Oceanic Engineering, 2021, 46(4): 1144−1154 doi: 10.1109/JOE.2021.3059210 [19] Chowdhary G, Mühlegg M, and Johnson E. Exponential parameter and tracking error convergence guarantees for adaptive controllers without persistency of excitation. International Journal of Control, 2014, 87(8): 1583−1603 doi: 10.1080/00207179.2014.880128 [20] Pan Y and Yu H. Composite learning robot control with guaranteed parameter convergence. Automatica, 2018, 89: 398−406 doi: 10.1016/j.automatica.2017.11.032 [21] Han T, Guan Z, Xiao B, and Yan H. Bipartite average tracking for multi-agent systems with disturbances: Finite-time and fixed-time convergence. IEEE Transactions on Circuits and Systems I: Regular Papers, 2021, 68(10): 4393−4402 doi: 10.1109/TCSI.2021.3104933 [22] Ma L and Zhu F. Fixed-time-synchronized bipartite time-varying formation tracking control of networked Euler-Lagrange systems. IEEE Transactions on Automation Science and Engineering, doi: 10.1109/TASE.2024.3395325 [23] Altafini C. Consensus problems on networks with antagonistic interactions. IEEE Transactions on Automatic Control, 2012, 58(4): 935−946 [24] Polyakov A. Nonlinear feedback design for fixed-time stabilization of linear control systems. IEEE Transactions on Automatic Control, 2011, 57(8): 2106−2110 [25] Chen M, Wang H, and Liu X. Adaptive fuzzy practical fixed-time tracking control of nonlinear systems. IEEE Transactions on Fuzzy Systems, 2019, 29(3): 664−673 [26] Liu K and Wang R. Antisaturation adaptive fixed-time sliding mode controller design to achieve faster convergence rate and its application. IEEE Transactions on Circuits and Systems II: Express Briefs, 2022, 69(8): 3555−3559 [27] Pan Y, Sun T, Joo Y H, and Yu H. Enhanced parameter estimation in adaptive control via online historical data. IET Control Theory & Applications, 2019, 13(16): 2710−2716 -
下载:
计量
- 文章访问数: 107
- HTML全文浏览量: 52
- 被引次数: 0
