Litcius/Paper detail

Fixed-Time Distributed Adaptive Formation Control for Multiple QUAVs With Full-State Constraints

Guozeng Cui, Hui Xu, Xinkai Chen, Jinpeng Yu

2023IEEE Transactions on Aerospace and Electronic Systems98 citationsDOI

Abstract

This article devises a fixed-time distributed adaptive formation control algorithm under the event-triggered framework to guarantee the expected formation pattern for multiple quadrotor unmanned aerial vehicles (QUAVs) with full-state constraints. The multiple QUAVs subject to full-state constraints are transformed into the ones that are free from any constraints via a time-varying nonlinear transformation function, which is effective to handle the case regardless of whether there exist state constraints. The issue of “explosion of complexity” as well as the singularity problem is fully coped via the fixed-time command filter and the smooth switch function, respectively. To further improve the control performance of multiple QUAVs, the nonsmooth error compensation mechanism is constructed to compensate the filtered error resulting from a command filter. The rigorous stability analysis of the developed event-trigger-based distributed formation control scheme proves that all signals of the closed-loop system are fixed-time bounded, and the states of multiple QUAVs will not violate the prescribed constraints and the formation tracking errors converge to a small region around the origin in a fixed time. Finally, simulation examples are performed to delineate the validity of the proposed distributed formation control algorithm.

Topics & Concepts

Control theory (sociology)Bounded functionComputer scienceFilter (signal processing)State (computer science)Transformation (genetics)SingularityTracking errorAdaptive controlStability (learning theory)Mathematical optimizationControl (management)MathematicsAlgorithmArtificial intelligenceComputer visionMathematical analysisGeneMachine learningChemistryBiochemistryDistributed Control Multi-Agent SystemsAdaptive Control of Nonlinear SystemsNeural Networks Stability and Synchronization