Mathematical Modelling and Fluidic Thrust Vectoring Control of a Delta Wing UAV

Ahsan Tanveer, Sarvat Mushtaq Ahmad*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Pitch control of an unmanned aerial vehicle (UAV) using fluidic thrust vectoring (FTV) is a relatively novel technique requiring no moving control surfaces, such as elevators. In this paper, the authors’ previous work on the characterization of a static co-flow FTV rig is further validated using the free to pitch dynamic test bench. The deflection of a primary jet after injection of a high-velocity secondary jet was captured using the tuft flow visualization technique, along with the experimental recording of subsequent normal force impinged on the Coanda surface resulting in the pitching moment. The effect of primary and secondary flow velocities on exhaust jet deflection, as well as on the pitch angle of the aircraft, is examined. Aerodynamic gain as well as the inertia of a delta wing UAV test bench are computed through experiments and fed into the equation of motion (e.o.m). The e.o.m developed aided in the design of a model-based PID controller for pitch motion control using the multi-parameter root locus technique. The root locus tuned controller serves as a benchmark controller for performance evaluation of the genetic algorithm (GA) and particle swarm optimization (PSO) tuned controllers. Furthermore, the frequency domain metric of gain and phase margins were also employed to reach a robust control design. Experiments conducted in a simulation environment reveal that PSO-PID results in a better response of the UAV in comparison to the baseline pitch controller.

Original languageEnglish
Article number563
JournalAerospace
Volume10
Issue number6
DOIs
StatePublished - Jun 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • Coanda effect
  • PID controller
  • dynamic model
  • fluidic thrust vectoring control
  • genetic algorithm optimization
  • particle swarm optimization
  • unmanned aerial vehicle

ASJC Scopus subject areas

  • Aerospace Engineering

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