Adaptive distributed consensus protocol for nonlinear multi-agent systems using sampled-data

In this study, an adaptive consensus problem is considered for leader-following multi-agent nonlinear uncertain systems with a directed communication topology. Existing works usually design the adaptive consensus controllers based on the relative states of neighboring agents, along with an adaptive law for adjusting the coupling weights between neighboring agents. Different adaptive laws were proposed for adjusting the coupling weights, such as adjusting by time-varying monotonically increasing functions, or adjusting discretely by the switch-logic rule, which have a complicated structure and require a lot of computation. To solve this shortcoming, a new adaptive consensus controller is proposed, where the adaptive coupling weights would be adjusted based on sampled-data of the relative states periodically. Compared with the existing adaptive consensus controllers, it is worth pointing out that the proposed controller can decrease the amount of computation and is more easy to implement in practice since the coupling weights are adjusted periodically. Moreover, the adaptive controller is fully distributed, which does not rely on any global information. A novel and concrete analysis is provided to guarantee the consensus of multi-agent systems, based on choosing proper piece-wise Lyapunov function. Numerical examples are given to illustrate the rightness of our result.