NDLI: Parameter estimation of vertical takeoff and landing aircrafts by using a PID controlling particle swarm optimization algorithm

Content Provider	Springer Nature Link
Author	Lu, Yongzhong Yan, Danping Levy, David
Copyright Year	2015
Abstract	As an indispensable constituent of the premises of highly precious control of vertical takeoff and landing (VTOL) aircrafts, parameter identification has received an increasingly considerable attention from academic community and practitioners. In an effort to tackle the matter better, we herewith put forward a PID controlling particle swarm optimizer (PSO) which we call the proportional integral derivative (PID) controller inspired particle swarm optimizer (P idSO). It uses a novel evolutionary strategy whereby a specified PID controller is used to improve particles’ local and global best positions information. Empirical experiments were conducted on both analytically unimodal and multimodal test functions. The experimental results demonstrate that PidSO features better search effectiveness and efficiency in solving most of the multimodal optimization problems when compared with other recent variants of PSOs, and its performance can be upgraded by adopting proper control law based controllers. Moreover, PidSO, together with least squares (LS) method and genetic algorithm (GA), is applied to the parameter estimation of the VTOL aircraft. In comparison with LS method and GA, PidSO is a more effective tool in estimating the parameters of the VTOL aircraft.
Starting Page	793
Ending Page	815
Page Count	23
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	44
Issue Number	4
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2015-11-19
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Proportional integral derivative controller Particle swarm optimizer Parameter identification Vertical takeoff and landing aircraft Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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