Combined Riccati-Genetic Algorithms Proposed for Non-Convex Optimization Problem Resolution – A Robust Control Model for PMSM

In this paper, is proposed a state feedback optimal control algorithm for uncertain linear systems, with norm bounded uncertainties. It is based on the use of Algebraic Riccati Equation – Genetic Algorithm (ARE-GA) developed for non-convex optimization problem resolution. The case of an uncertain Pe...

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Bibliographic Details
Published in:Studies in Informatics and Control 2015, Vol.24 (3), p.317
Main Authors: DCHICH, Khira, ZAAFOURI, Abderrahmen, CHAARI, Abdelkader
Format: Article
Language:English
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Summary:In this paper, is proposed a state feedback optimal control algorithm for uncertain linear systems, with norm bounded uncertainties. It is based on the use of Algebraic Riccati Equation – Genetic Algorithm (ARE-GA) developed for non-convex optimization problem resolution. The case of an uncertain Permanent Magnet Synchronous Motor (PMSM) based on the use of an Extended Kalman Filter (EKF) to estimate both position and speed, without any mechanical sensor is considered to illustrate the efficiency of the proposed technique.
ISSN:1220-1766
1841-429X
DOI:10.24846/v24i3y201509