@inproceedings{66b4ef7eae994283b04adfa05e1691ef,
title = "Flying vehicle longitudinal controller design via prey-predator pigeon-inspired optimization",
abstract = "Prey-Predator Pigeon-Inspired Optimization (PPPIO) is a new bio-inspired swarm intelligence algorithm which combines the standard PIO algorithm and the prey predator strategy to improve the optimal solution obtained from the PIO algorithm. PIO algorithm can easily trap into a local optimal solution, which is the main defect that limits its further application. To overcome this defect, a Prey-Predator PIO algorithm is proposed. This paper addresses both PIO and PPPIO in finding the optimal values for control system gains of tactical missile longitudinal autopilot. The control system gains are calculated at first the classical control techniques and then both PIO and PPPIO algorithms are utilized to find out optimal values for these gains which improves system performance and stability margins. Simulation is used to declare the efficiency of each algorithm.",
keywords = "Flying Vehicle, Pigeon-Inspired Optimization (PIO), Prey-Predator Pigeon-Inspired Optimization (PPPIO), Proportion-Integral (PI), classical control, longitudinal autopilot, missile, parameter adjustment",
author = "Mohamed, \{Mostafa S.\} and Haibin Duan and Li Fu",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 ; Conference date: 27-11-2017 Through 01-12-2017",
year = "2017",
month = jul,
day = "1",
doi = "10.1109/SSCI.2017.8280822",
language = "英语",
series = "2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings",
address = "美国",
}