Design Fixed-Time Practical Distributed Average Tracking Algorithms for Nonlinear Signals With Bounded- and Lipschitz-Type Derivatives
Qiang Chen, Guoqing Shi, Yu Zhao, Guanghui Wen
Abstract
In this brief, the fixed-time practical distributed average tracking (DAT) problem for multiple nonlinear signals is studied, where the nonlinear signals are with bounded- and Lipschitz-type derivatives respectively. Two fixed-time practical DAT algorithms are proposed for agents with local interactions to track the average of the multiple nonlinear signals within a fixed convergence time by using time-base generator techniques. Different from existing results, the proposed DAT algorithms in this brief are applicable to DAT problems for nonlinear signals with both bounded- and Lipschitz-type derivatives, which keeps more reality. Also, the proposed fixed-time practical DAT algorithms are able to provide an explicit estimation of the upper-bounded convergence time without dependence on initial conditions. The fixed convergence time in the proposed algorithms can be adjusted more flexibly. Finally, some illustrative examples are shown to manifest the validity of the fixed-time practical DAT algorithms.