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Research Papers

Automated Identification of a Mechatronic System Model Using Genetic Programming and Bond Graphs

[+] Author and Article Information
Saeed Behbahani

Department of Mechanical Engineering,
Isfahan University of Technology,
Isfahan 84156-83111, Iran

Clarence W. de Silva

Fellow ASME
Department of Mechanical Engineering,
The University of British Columbia,
Vancouver, BC V6T 1Z4, Canada
e-mail: desilva@mech.ubc.ca

1Corresponding author.

Contributed by Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT AND CONTROL. Manuscript received January 12, 2007; final manuscript received February 2, 2013; published online May 29, 2013. Assoc. Editor: J. Karl Hedrick.

J. Dyn. Sys., Meas., Control 135(5), 051007 (May 29, 2013) (12 pages) Paper No: DS-07-1015; doi: 10.1115/1.4024171 History: Received January 12, 2007; Revised February 02, 2013

Modeling, identification (experimental modeling), and design of dynamic systems and the associated problem of controller design are common problems in the field of mechatronics. A typical mechatronic problem entails finding the best topology as well as parameter values of the desired solution. In view of dynamic interactions in a mechatronic system, which involves more than one domain, it is desirable to use concurrent and integrated methodology in the solution. The powerful search ability of genetic programming (GP) along with the domain independence and the open architecture of bond graph (BG) modeling can be integrated to develop an evolutionary mechatronic tool for identification of a complex mechatronic system. This paper extends this integrated approach to nonlinear mechatronic problems and develops a software tool for this purpose. It is illustrated how the developed technique and the corresponding software tool can be used in the automated synthesis and identification of a nonlinear mechatronic system. The performance of the software tool is validated by applying it to a nonlinear electrohydraulic manipulator, which falls into the class of multidomain systems. The results obtained from the application are quite encouraging, and form a rationale for the extension of the tool for concurrent and optimal design of mechatronic systems.

Copyright © 2013 by ASME
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References

Figures

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Fig. 5

An example of function generation

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Fig. 4

Function generation for nonlinear elements

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Fig. 3

Insert-junction construction function

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Fig. 2

Add-element construction function

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Fig. 1

A sample BG embryo model

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Add MDOF construction function

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Add-friction construction function

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Fig. 8

Equivalence system of backlash

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Fig. 9

The performance of the developed tool in evolutionary modeling of a system

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Fig. 10

Embryo and evolved model in evolutionary system identification of a vibratory system

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Fig. 11

Schematic diagram of the electrohydraulic manipulator

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Fig. 12

Displacement response under square excitation, used for system identification of the mechanical part in the electrohydraulic manipulator

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Fig. 13

Displacement response under sinusoidal excitation, used for verification of the identified model

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Fig. 14

Schematic diagram showing the components of the electrohydraulic manipulator

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Fig. 15

The embryo bond graph model of the electrohydraulic manipulator

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Fig. 16

Schematic diagram of the hydraulic cylinder

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Fig. 17

Schematic diagram and the bond graph model of the servo valve tongue controller

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Fig. 18

Pressure responses under square excitation, used for system identification of the electrohydraulic manipulator; (a) P1; (b) P2

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Fig. 19

Pressure responses under sinusoidal excitation, used for verification of the identified model; (a) P1; (b) P2

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