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Position Control of Nonlinear Crane Systems using Dynamic Neural Network  

Han, Seong-Hun (동아대학교 전기공학과)
Cho, Hyun-Cheol (동아대학교 전기공학과)
Lee, Kwon-Soon (동아대학교 전기공학과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.56, no.5, 2007 , pp. 966-972 More about this Journal
Abstract
This paper presents position control of nonlinear three-dimensional crane systems using neural network approach. Such crane system generally includes very complicated characteristic dynamics and mechanical framework such that its mathematical model is expressed by strong nonlinearity. This leads difficulty in control design for the systems. We linearize the nonlinear system model to construct PID control applying well-known linear control theory and then neural network is utilized to compensate system perturbation due to linearization. Thus, control input of the crane system is composed of nominal PID and neural output signals respectively. Our method illustrates simple design procedure, but system perturbation and modelling error are overcome through a neural compensator. As well. adaptive neural control is constructed from online learning. Computer simulation demonstrates our control approach is superior to the classic control systems.
Keywords
Recurrent neural network; Nonlinear crane systems; Online learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
Times Cited By SCOPUS : 0
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