Technical Briefs

Parameter Identification for Electrohydraulic Valvetrain Systems

[+] Author and Article Information
James Gray

Miroslav Krstic1

Department of Mechanical and Aerospace Engineering,  University of California, San Diego La Jolla, CA 92093-0411 e-mail: krstic@ucsd.edu

Nalin Chaturvedi

Research Engineer  Bosch Research and Technology Center North America, Palo Alto, CA 94304 e-mail: Nalin.Chaturvedi@us.bosch.com


Corresponding author.

J. Dyn. Sys., Meas., Control 133(6), 064502 (Nov 21, 2011) (8 pages) doi:10.1115/1.4004780 History: Received April 16, 2009; Revised May 03, 2011; Published November 21, 2011; Online November 21, 2011

We consider an electrohydraulic valve system (EHVS) model with uncertain parameters that may possibly vary with time. This is a nonlinear third order system consisting of two clearly separated subsystems, one for the piston position and the other for the chamber pressure. The nonlinearities involved are flow-pressure characteristics of the solenoid valves, the pressure dynamics of the chamber due to varying volume, and a variable damping nonlinearity. We develop a parametric model that is linear in the unknown parameters of the system using filtering. We deal with a nonlinear parameterization in the variable damping term using the Taylor approximation. We design a parameter identifier, which employs a continuous-time unnormalized least-squares update law with a forgetting factor. This update law exponentially converges to the true parameters under a persistence of excitation condition, which is satisfied due to the periodic regime of operation of EHVS. We present simulation results that show good following of unknown parameters even with the presence of sensor noise.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 1

Schematic of the EHVS system

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Figure 2

Dependence of D(Db+xp2k) on D, Db , k, and xp

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Figure 3

Estimator block diagram

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Figure 4

Measured system states in transient

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Figure 5

Results of the test to determine the persistence of excitation of the regressors

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Figure 6

Parameters estimates θ̂1 and the associated Γ1 diagonal terms

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Figure 7

Parameters estimates θ̂2 and the associated Γ2 diagonal terms



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