Design of uav robust autopilot based on adaptive neuro-fuzzy inference system

Authors

  • Mohand Achour Touat
  • Anatoly A. Tunik

DOI:

https://doi.org/10.18372/2306-1472.37.1624

Abstract

 This paper is devoted to the application of adaptive neuro-fuzzy inference systems to the robust control of the UAV longitudinal motion. The adaptive neore-fuzzy inference system model needs to be trained by input/output data. This data were obtained from the modeling of a ”crisp” robust control system. The synthesis of this system is based on the separation theorem, which defines the structure and parameters of LQG-optimal controller, and further - robust optimization of this controller, based on the genetic algorithm. Such design procedure can define the rule base and parameters of fuzzyfication and defuzzyfication algorithms of the adaptive neore-fuzzy inference system controller, which ensure the robust properties of the control system. Simulation of the closed loop control system of UAV longitudinal motion with adaptive neore-fuzzy inference system controller demonstrates high efficiency of proposed design procedure.

Author Biographies

Mohand Achour Touat

post-graduate student (Algeria)

Anatoly A. Tunik

D.E., prof.

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How to Cite

Touat, M. A., & Tunik, A. A. (2008). Design of uav robust autopilot based on adaptive neuro-fuzzy inference system. Proceedings of the National Aviation University, 37(4), 9–17. https://doi.org/10.18372/2306-1472.37.1624

Issue

Section

AEROSPACE SYSTEMS FOR MONITORING AND CONTROL