Distributed Average Tracking for Lipschitz-Type Nonlinear Dynamical Systems
Distributed Average Tracking for Lipschitz-Type Nonlinear Dynamical Systems
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发表时间：
2017-04
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通讯作者：
Yu Zhao;Yongfang Liu
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作者：
Yu Zhao;Yongfang Liu
研究了Lipschitz型非线性动力系统的分布平均跟踪问题。我们的目标是设计分布式平均跟踪算法，本地交互代理跟踪多个参考信号的平均值。在这里，在代理和参考信号的动力学，有一个非线性项满足Lipschitz型条件。设计了三种分布式平均跟踪算法。首先，基于状态相关增益设计方法，提出了一种鲁棒分布平均跟踪算法，解决了分布平均跟踪问题，且不需要相同的初始条件。其次，通过使用增益自适应方案，本文提出了一种自适应分布式平均跟踪算法，以消除Lipschitz常数已知的要求。第三，为了减小抖振，使算法更易于实现，进一步设计了一种基于时变边界层的连续分布平均跟踪算法，作为对已有的非连续分布平均跟踪算法的连续逼近。
In this paper, a distributed average tracking problem is studied for Lipschitz-type nonlinear dynamical systems. The objective is to design distributed average tracking algorithms for locally interactive agents to track the average of multiple reference signals. Here, in both the agents' and the reference signals' dynamics, there is a nonlinear term satisfying the Lipschitz-type condition. Three types of distributed average tracking algorithms are designed. First, based on state-dependent-gain designing approaches, a robust distributed average tracking algorithm is developed to solve distributed average tracking problems without requiring the same initial condition. Second, by using a gain adaption scheme, an adaptive distributed average tracking algorithm is proposed in this paper to remove the requirement that the Lipschitz constant is known for agents. Third, to reduce chattering and make the algorithms easier to implement, a continuous distributed average tracking algorithm based on a time-varying boundary layer is further designed as a continuous approximation of the previous discontinuous distributed average tracking algorithms.