Research Papers: Design Automation

Vehicle Suspension Identification Via Algorithmic Computation of State and Design Sensitivities

[+] Author and Article Information
Alfonso Callejo

Research Scholar
National Institute for Aviation Research,
Wichita State University,
Wichita, KS 67260
e-mail: alcallejo@gmail.com

Javier García de Jalón

Applied Mathematics Professor
Universidad Politécnica de Madrid,
Madrid 28006, Spain
e-mail: javier.garciadejalon@upm.es

MADYMO is a registered trademark of TASS International.

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1Previous affiliation: INSIA (Universidad Politécnica de Madrid, Madrid 28031, Spain).

2Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received July 6, 2014; final manuscript received October 20, 2014; published online December 8, 2014. Assoc. Editor: Ettore Pennestri.

J. Mech. Des 137(2), 021403 (Feb 01, 2015) (9 pages) Paper No: MD-14-1399; doi: 10.1115/1.4029027 History: Received July 06, 2014; Revised October 20, 2014; Online December 08, 2014

It is common in mechanical simulation to not know the value of key system parameters. When the simulation is very sensitive to those design parameters and practical or budget limitations prevent the user from measuring the real values, parameter identification methods become essential. Kalman filter methods and optimization methods are the most widespread approaches for the identification of unknown parameters in multibody systems. A novel gradient-based optimization method, based on sensitivity analyses for the computation of machine-precision gradients, is presented in this paper. The direct differentiation approach, together with the algorithmic differentiation of derivative terms, is employed to compute state and design sensitivities. This results in an automated, general-purpose and robust method for the identification of parameters. The method is applied to the identification of a real-life vehicle suspension system (namely of five stiffness coefficients) where both smooth and noisy reference responses are considered. The identified values are very close to the reference ones, and everything is carried out with limited user intervention and no manual computation of derivatives.

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Fig. 3

Steering function in the double lane-change maneuver

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Fig. 4

Plan view of the double lane-change maneuver

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Fig. 5

Front view of the double lane-change maneuver

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Fig. 2

(a) Air spring and (b) damper forces

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Fig. 1

(a) Front and (b) rear suspension systems

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Fig. 6

Two example sensitivities (continuous lines) with their corresponding coordinates (dashed lines) and ND error (double lane-change maneuver)

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Fig. 9

History of design parameter values (smooth references)

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Fig. 10

Comparison of reference, identified and unidentified responses (smooth references)

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Fig. 7

Parameter identification flowchart

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Fig. 8

Objective function values (smooth (a) and noisy (b) cases)

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Fig. 11

History of design parameter values (noisy references)

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Fig. 12

Comparison of reference, identified and unidentified responses (noisy references)

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Fig. 13

Detail view of reference, identified and unidentified responses (noisy references)



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