Nonrepetitive Leader–Follower Formation Tracking for Multiagent Systems With LOS Range and Angle Constraints Using Iterative Learning Control
提出一种迭代学习控制算法，解决非线性多智能体系统在视线距离和角度约束下的领航-跟随编队跟踪问题，允许参考轨迹随迭代变化，并处理执行器故障和系统不确定性。
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.