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JOURNALS // Upravlenie Bol'shimi Sistemami // Archive

UBS, 2022 Issue 96, Pages 31–48 (Mi ubs1104)

This article is cited in 2 papers

Analysis and Synthesis of Control Systems

Synthesis of a reduced order observer for all-drive electromechanical system

D. Krasnov

V.A. Trapeznikov Institute of Control Sciences of RAS, Moscow

Abstract: In a deterministic formulation, the problem of observing non-measurable variables of an electromechanical system operating under conditions of parametric uncertainty is considered. For the case when the sensors are located only on electrical actuators, the conditions are formalized under which the problem of observing the entire state vector has a solution without increasing the dynamic order of the closed-loop system due to regressors and identifiers of undefined parameters. As a basis for constructions, an approach to assessing external disturbances acting on an object is adopted, which does not need to use dynamic models of external influences. Within the framework of this approach, for the considered electromechanical system, the structure of a reduced robust state observer is substantiated. Unlike the standard reduced Luenberger observer, which does not use differential equations of measured variables, the proposed observer does not use differential equations describing the dynamics of unmeasured state variables, which are assumed to be external bounded perturbations in solving the observation problem. A decomposition procedure for adjusting the parameters of piecewise linear feedbacks in an observer is developed, which provides stabilization with a given accuracy for a given time of observation errors and their derivatives. It is shown that the estimated signals of unmeasured variables are the corresponding variables and the control actions of the observer.

Keywords: electromechanical system, robustness, reduced state observer, decomposition, piecewise linear feedback.

UDC: 62.50
BBK: 32.817

Received: November 12, 2021
Published: March 31, 2022

DOI: 10.25728/ubs.2022.96.3



© Steklov Math. Inst. of RAS, 2026