IEEE Trans Cybern. 2019 May;49(5):1748-1758. doi: 10.1109/TCYB.2018.2817610. Epub 2018 Apr 2.
In this paper, we present a novel iterative learning control (ILC) algorithm for the leader-follower formation tracking problem of a class of nonlinear multiagent systems that are subject to actuator faults. Unlike most ILC works that require identical reference trajectories over the iteration domain, the desired line-of-sight (LOS) range and angle profiles can be iteration dependent based on different tasks and environment in each iteration. Furthermore, the LOS range and angle tracking errors are subject to iteration and time dependent constraint requirements. Both parametric and nonparametric system unknowns and uncertainties, in particular the control input gain functions that are not fully known, are considered. We show that under the proposed algorithm, the formation tracking errors can converge to zero uniformly over the iteration domain beyond a certain time interval in each iteration, while the constraint requirements on the LOS range and angle will not be violated during operations. A numerical simulation involving two agents in leader-follower formation is presented in the end to demonstrate the efficacy of the proposed algorithm.
在本文中，我们提出了一种新颖的迭代学习控制（ILC）算法，用于解决一类存在执行器故障的非线性多智能体系统的领导者-跟随者编队跟踪问题。与大多数迭代学习控制工作不同，大多数迭代学习控制工作要求在迭代域上具有相同的参考轨迹，而我们提出的算法中，期望的视线（LOS）范围和角度曲线可以根据每个迭代中的不同任务和环境而依赖于迭代。此外，LOS 范围和角度跟踪误差受到迭代和时间相关约束要求的限制。我们考虑了参数和非参数系统的未知和不确定性，特别是不完全已知的控制输入增益函数。我们证明，在提出的算法下，在每个迭代中的某个时间间隔之后，编队跟踪误差可以在迭代域上一致地收敛到零，而在操作过程中不会违反 LOS 范围和角度的约束要求。最后，提出了一个涉及两个在领导者-跟随者编队中的智能体的数值仿真，以验证所提出算法的有效性。
