Adaptive Neural FOPID Controller Applied for Missile Guidance System


Yaghi M., EFE M. Ö.

43rd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON), Beijing, China, 29 October - 01 November 2017, pp.2955-2960 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/iecon.2017.8216499
  • City: Beijing
  • Country: China
  • Page Numbers: pp.2955-2960

Abstract

Adaptive Neural Fractional Order Proportional Integral Derivative (FOPID) controller is designed to control a missile using Proportional Navigation Guidance PNG system. The proposed FOPID controller is intended to improve the performance of PNG system in terms of miss distance accuracy and stability. A new tuning procedure has been proposed by applying hybrid neural genetic algorithm in order to align the missile attitude with the Line of Sight (LOS) angle. Genetic algorithm is used first in which it has fast convergence speed at the initial tuning stages, but near the global optimum values the tuning process becomes very slow, therefore neural technique is used to train a neural network which has faster tuning speed near the optimal values, and the tuning accuracy becomes much better. The tuning method has been compared with Ziegler Nichols which is applied on PID controller and the results showed the superiority of the proposed tuning method. The need for fractional order type of feedback control system is justified by the nonlinear nature of the proportional navigation system and the dynamics of the missiles, to which many alternatives were applied in the literature using less accurate controllers while the proposed control system proved to have more accuracy hitting the target with less value of miss distance and more stability in the angle of attack and the motion during flight as well as the performance of the controller in terms of second and infinity norms.