Distributed Average Tracking for Lipschitz-Type Nonlinear Dynamical Systems
For multi-agent systems with nonlinear dynamics, this work provides distributed tracking solutions that relax restrictive assumptions, but the results are incremental as they extend existing methods to a specific nonlinear class.
This paper designs distributed average tracking algorithms for Lipschitz-type nonlinear dynamical systems, enabling agents to track the average of multiple reference signals without requiring identical initial conditions or known Lipschitz constants. Three algorithms are proposed: a robust state-dependent-gain method, an adaptive method, and a continuous approximation to reduce chattering.
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.