Parameterized Uncertainty Model Using a Genetic Algorithm With Application to an Electro-Hydraulic Valve Control System | Zendy

Zuheng Kang | Zendy; Bahaa I. Kazem | Zendy; Roger Fales | Zendy

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Parameterized Uncertainty Model Using a Genetic Algorithm With Application to an Electro-Hydraulic Valve Control System

Author(s) -

Zuheng Kang,

Bahaa I. Kazem,

Roger Fales

Publication year - 2015

Publication title -

mospace institutional repository (university of missouri)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1115/dscc2015-9994

Subject(s) - parameterized complexity , genetic algorithm , stability (learning theory) , computer science , control theory (sociology) , work (physics) , class (philosophy) , control system , robust control , hydraulic machinery , algorithm , regular polygon , mathematical optimization , control (management) , mathematics , artificial intelligence , engineering , machine learning , mechanical engineering , geometry , electrical engineering

This work proposes a new method of determining a parameterization of an uncertainty model using a genetic algorithm. A genetic algorithm is used in a unique way to solve the non-convex parameterization problem in this work. The methods presented here are demonstrated on an electrohydraulic valve control system problem. This demonstration includes parameterizing an uncertainty class determined from test data for 30 replications of an electrohydraulic flow control valve. The parameterization of the uncertainty is used to analyze the robust stability of a control system for a class of valves.Copyright © 2015 by ASME

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