Intermittent Output Based Fuzzy Adaptive Control of Uncertain Nonlinear Multi-Agent Systems With Its Application to Robotic Systems

It is difficult to ensure tracking performance even for general nonlinear systems under the condition that only intermittent output signals are used. This paper studies the problem of adaptive fuzzy consensus tracking for a class of mismatched nonlinear strict-feedback multi-agent systems (MASs) by using intermittent output signals. Firstly, a novel fuzzy state observer using intermittent output states and fuzzy-logic systems is constructed to estimate the unknown state variables of the system. Secondly, dynamic filtering technology is applied to address the issue of virtual controller non-differentiability caused by intermittent output feedback during the backstepping design process. Furthermore, for the situation where the controller is non-differentiable in stability analysis, an alternative solution can be proposed: initially, a distributed continuous control strategy is developed using conventional continuous output signals, and then the output signals in the previous scheme are replaced with event-triggered signals to design a distributed event-triggered control scheme. It is demonstrated that the designed event-triggered control scheme guarantees that all closed-loop system signals remain semi-globally uniformly ultimately bounded (SGUUB), and the distributed consensus tracking errors can converge to a neighborhood of zero. Finally, the effectiveness of the proposed scheme is verified through simulations involving a group of single-link robots.