Research on Neural Network PID Quadratic Optimal Controller in Active Magnetic Levitation
Zhongqiao Zheng*, 1, 2, Xiaojing Wang1, Yanhong Zhang2, Jiangsheng Zhang2
Identifiers and Pagination:Year: 2014
First Page: 42
Last Page: 47
Publisher Id: TOMEJ-8-42
Article History:Received Date: 11/11/2013
Revision Received Date: 11/02/2014
Acceptance Date: 03/03/2014
Electronic publication date: 21/3/2014
Collection year: 2014
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
In response to the uncertainty, nonlinearity and open-loop instability of active magnetic levitation control system, a neural network PID quadratic optimal controller has been designed using optimum control theory. By introducing supervised Hebb learning rule, constraint control for positioning errors and control increment weighting are realized by adjusting weighting coefficients, using weighed sum-squares of the control increment and the deviation between actual position and equilibrium position of the rotor in active magnetic levitation system as objective function. The simulation results show that neural network PID quadratic optimal controller can maintain the stable levitation of rotor by effectively improving static and dynamic performances of the system, so as to maintain the stable levitation of rotor in active magnetic levitation system which has stronger anti-jamming capacity and robustness.