Event-Triggered Distributed Fixed-Time Adaptive Attitude Control With Prescribed Performance for Multiple QUAVs

This article concentrates on the distributed fixed-time adaptive event-triggered attitude control problem for multiple quad-rotor unmanned aerial vehicles (QUAVs) with prescribed performance. By utilizing the fuzzy logic system and constructing a piecewise continuous function, the unknown nonlinear dynamics of multiple QUAVs and the problem of singularity are skillfully addressed, respectively. The command filter that has fixed-time convergence is devised to avert the “explosion of complexity” problem, while the impact of filtered error is eliminated by virtue of the fractional-power-based error compensation signals. Moreover, a fixed-time performance function is embedded into the distributed attitude control algorithm to ensure that the synchronization errors converge to the preassigned performance confines. It is strictly proved that all closed-loop signals are fixed-time bounded, and the disagreement errors are steered into a small region nearby the zero in a fixed time. Finally, numerical simulations are given to demonstrate the efficiency and superiority of the devised fixed-time control scheme. Note to Practitioners—This article aims at designing an event-triggered distributed attitude control algorithm to relax the communication burden for multiple QUAVs subject to external disturbances. In practical applications, the communication bandwidth and the onboard energy of QUAVs are limited, while the traditional time-triggered approaches neglect these realistic restrictions. Thereby, by incorporating a relative threshold event-triggered mechanism into the command filtered backstepping design process, not only can the “explosion of complexity” issue and the impact of filtered error be surmounted, but also the frequency of controller updating is reduced. Additionally, the construction of a prescribed performance function with fixed-time convergence results in the improvement of both transient and steady-state performances for multiple QUAVs, thus meeting practical requirements more effectively.