Title :
Control of an airship using particle swarm optimization and neural network
Author :
Jia, Ruting ; Frye, Michael T. ; Qian, Chunjiang
Author_Institution :
Electr. & Comput. Eng. Dept., Univ. of Texas at San Antonio, San Antonio, TX, USA
Abstract :
The objective of this paper is to design an optimized controller for the tri-turbofan airship model. In lieu of using the traditional controller analysis method, the particle swarm optimization algorithm for controller optimization has been implemented. For more accurate results, this research used an updated neural network model to approximate the actual tri-turbofan airship dynamics. The effectiveness of the PSO algorithm will be shown by the simulation in an updated neural network model, compared to a linear model of the tri-turbofan model.
Keywords :
aerospace computing; aircraft control; control system analysis; control system synthesis; neural nets; optimal control; particle swarm optimisation; PSO algorithm; controller optimization; neural network; particle swarm optimization; real time optimal control; tri-turbofan airship dynamics; tri-turbofan airship model; Cellular phones; Design engineering; Design optimization; Evolutionary computation; Neural networks; Optimal control; Particle swarm optimization; Surveillance; USA Councils; Vehicle dynamics; Particle swarm optimization; dynamic neural network model; real time optimal control;
Conference_Titel :
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4244-2793-2
Electronic_ISBN :
1062-922X
DOI :
10.1109/ICSMC.2009.5346862