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Research Papers

Estimation Design Using Youla Parametrization With Automotive Applications

[+] Author and Article Information
Francis Assadian

Professor
Mem. ASME
Department of Mechanical and
Aerospace Engineering,
University of California, Davis,
Davis, CA 95616
e-mail: fassadian@ucdavis.edu

Alex K. Beckerman

Mem. ASME
Department of Mechanical and
Aerospace Engineering,
University of California, Davis,
Davis, CA 95616
e-mail: akbecker@ucdavis.edu

Jose Velazquez Alcantar

Mem. ASME
Department of Mechanical and
Aerospace Engineering,
University of California, Davis,
Davis, CA 95616
e-mail: jvelazquez@ucdavis.edu

1Corresponding author.

2Present address: Ford Motor Company, Dearborn, MI 48126.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received November 30, 2017; final manuscript received December 19, 2017; published online March 16, 2018. Editor: Joseph Beaman.

J. Dyn. Sys., Meas., Control 140(8), 081015 (Mar 16, 2018) (11 pages) Paper No: DS-17-1593; doi: 10.1115/1.4039157 History: Received November 30, 2017; Revised December 19, 2017

Youla parametrization is a well-established technique in deriving single-input single-output (SISO) and, to a lesser extent, multiple-input multiple-ouput (MIMO) controllers (Youla, D., Bongiorno, J. J., Jr., and Lu, C., 1974, “Singleloop Feedback-Stabilization of Linear Multivariable Dynamical Plants,” Automatica, 10(2), pp. 159–173). However, the utility of this methodology in estimation design, specifically in the framework of controller output observer (COO) (Ozkan, B., Margolis, D., and Pengov, M., 2008, “The Controller Output Observer: Estimation of Vehicle Tire Cornering and Normal Forces,” ASME J. Dyn. Syst., Meas., Control, 130(6), p. 061002), is not established. The fundamental question to be answered is as follows: is it possible to design a deterministic estimation technique using Youla paramertization with the same robust performance, or better, than well-established stochastic estimation techniques such as Kalman filtering? To prove this point, at this stage, a comparative analysis between Youla parametrization in estimation and Kalman filtering is performed through simulations only. In this paper, we provide an overview of Youla parametrization for both control and estimation design. We develop a deterministic SISO and MIMO Youla estimation technique in the framework of COO, and we investigate the utility of this method for two applications in the automotive domain.

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References

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Figures

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Fig. 2

Classical observer design: (a) classical observers and (b) observer transformation from states to input

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Fig. 1

Automotive application for COO

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Fig. 5

Comparison of classical feedback loop and Youla feedforward transformation: (a) classical feedback loop and (b) Youla feedforward transformation

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Fig. 9

SISO state estimation with D-road PSD input: (a) Wheel stroke and (b) unsprung mass velocity

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Fig. 10

SISO state estimation with a speed bump input: (a) wheel stroke and (b) unsprung mass velocity

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Fig. 8

Closed-loop frequency response

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Fig. 14

MIMO state estimation with D-road PSD input—unsprung mass velocity

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Fig. 11

Bode for decoupled system

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Fig. 12

Bode for the coupled system

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Fig. 13

MIMO state estimation with D-road PSD input—sprung mass velocity

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Fig. 15

MIMO state estimation with D-road PSD input—wheel stroke

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Fig. 16

MIMO state estimation with D-road PSD input—tire deflection

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Fig. 17

Damping force feedback control structure

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Fig. 18

Actual versus estimated damping force over a motorway at 100 KPH

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Fig. 19

PSD of road profile for a motorway (bottom) and farm road (top)

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