Fully distributed hybrid adaptive learning consensus protocols for a class of non-linearly parameterized multi-agent systems

摘要

The fully distributed hybrid adaptive learning consensus problem for a class of non-linearly parameterized multi-agent systems is investigated in this paper. Under the alignment initial condition and by parameter separation technique, Barbalat-like lemma and a novel Lyapunov–Krasovskii functional, the hybrid adaptive learning consensus protocols with time-varying adaptive control gains and differential-difference learning updating laws are presented, which are fully distributed, and the perfect consensus tracking is guaranteed over a finite time interval. Finally, two simulation examples are given to verify the availability and practicability of theoretical results.