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

Khira DCHICH, Abderrahmen ZAAFOURI, Abdelkader CHAARI
University of Tunis, Unit C3S,
Higher School of Sciences and Techniques of Tunis (ESSTT),
5 Av. Taha Hussein, BP 56, 1008 Tunis, Tunisia
Khira.Dchich@fsb.rnu.tn, abderrahmen.zaafouri@ isetr.rnu.tn, assil.chaari@esstt.rnu.tn

Abstract: 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.

Keywords: Quadratic Stabilization, Uncertain System, Riccati Equation, Convex, Non-convex, Genetic Algorithm, Permanent Magnet Synchronous Motor, Extended Kalman Filter.

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CITE THIS PAPER AS:
Khira DCHICH, Abderrahmen ZAAFOURI, Abdelkader CHAARI, Combined Riccati-Genetic Algorithms Proposed for Non-Convex Optimization Problem Resolution – A Robust Control Model for PMSM, Studies in Informatics and Control, ISSN 1220-1766, vol. 24 (3), pp. 317-328, 2015. https://doi.org/10.24846/v24i3y201508