Adaptive Consensus of Non-Strict Feedback Switched Multi-Agent Systems With Input Saturations
Zhanjie Li, Jun Zhao
Abstract
This paper considers the leader-following consensus for a class of nonlinear switched multi-agent systems (MASs) with non-strict feedback forms and input saturations under unknown switching mechanisms. First, in virtue of Gaussian error functions, the saturation nonlinearities are represented by asymmetric saturation models. Second, neural networks are utilized to approximate some unknown packaged functions, and the structural property of Gaussian basis functions is introduced to handle the non-strict feedback terms. Third, by using the backstepping process, a common Lyapunov function is constructed for all the subsystems of the followers. At last, we propose an adaptive consensus protocol, under which the tracking error under arbitrary switching converges to a small neighborhood of the origin. The effectiveness of the proposed protocol is illustrated by a simulation example.