Fixed-time Bipartite Formation Tracking Control for Multi-USV Systems With Uncertain Model Parameters
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摘要: 针对模型参数不确定下多无人艇(Multiple unmanned surface vehicle, Multi-USV)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(Persistent excitation, PE)条件的区间激励(Interval excitation, IE)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 method, this paper proposes a new class of distributed fixed-time bipartite formation tracking control protocols to address the fixed-time bipartite formation tracking control problem for multiple unmanned surface vehicle (Multi-USV) systems with uncertain model parameters. Firstly, the command filtering technique is introduced into the backstepping control method where the virtual and real control protocols are designed, respectively. On this basis, composite learning laws are designed to estimate unknown model parameters. Specifically, prediction errors are generated by using both online recorded data and real-time data, and parameter estimates are updated by utilizing both tracking errors and prediction errors. The results demonstrate that under interval excitation (IE) conditions, which are less stringent than persistent excitation (PE) conditions, the proposed control protocols ensure fixed-time convergence of both formation errors and parameter estimation errors, effectively solving the fixed-time bipartite formation tracking control problem for multi-USV systems. Finally, the effectiveness of the proposed control protocols is demonstrated by performing simulation experiments.
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图 3 参数估计误差 $\tilde{\eta}_{0i}(t),\; i=1,\;2,\;\cdots,\;7$
Fig. 3 Parameter estimation errors $\tilde{\eta}_{0i}(t),\; i=1,\;2,\;\cdots,\;7$
图 4 参数估计误差$\tilde{\Theta}_{i}(t),\; i=1,\;2,\;\cdots,\;7$
Fig. 4 Parameter estimation errors $\tilde{\Theta}_{i}(t),\; i=1,\;2,\;\cdots,\;7$
图 5 局部跟踪误差$\epsilon_{i1}(t),\; \epsilon_{i2}(t),\; i=1,\;2,\;\cdots,\;7$
Fig. 5 Local tracking errors $\epsilon_{i1}(t),\;$ $\epsilon_{i2}(t),\;$ $i=1,\;2,\;\cdots,\;7$
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