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research-article

Estimation Design using Youla Parametrization with Automotive Applications

[+] Author and Article Information
Francis Assadian

Professor, Member of ASME, Department of Mechanical & Aerospace Engineering, University of California, Davis, California 95616
fassadian@ucdavis.edu

Alex Beckerman

Graduate Student Researcher, Member of ASME, Department of Mechanical & Aerospace Engineering, University of California, Davis, California 95616
akbecker@ucdavis.edu

Jose Velazquez Alcantar

Graduate Student Researcher, Member of ASME, Department of Mechanical & Aerospace Engineering, University of California, Davis, California 95616
jvelazquez@ucdavis.edu

1Corresponding author.

ASME doi:10.1115/1.4039157 History: Received November 30, 2017; Revised December 19, 2017

Abstract

Youla parametrization is a well established technique in deriving SISO (Single-Input Single- Output) and, to a lesser extent, MIMO (Multiple-Input Multiple-Ouput)controllers [1]. However, the utility of this methodology in estimation design, specifically in the framework of Controller Output Observer [2] (COO), 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 article, 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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