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System identification and robust controller design using genetic algorithms for flexible space structures / Marco P. Schoen in Transactions of the ASME . Journal of dynamic systems, measurement, and control, Vol. 131 N° 3 (Mai 2009) 

Public ISBD [article]  in Transactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 131 N° 3 (Mai 2009) . - 11 p. Titre : System identification and robust controller design using genetic algorithms for flexible space structures Type de document : texte imprimé Auteurs : Marco P. Schoen, Auteur ; Randy C. Hoover, Auteur ; Sinchai Chinvorarat, Auteur Année de publication : 2009 Article en page(s) : 11 p. Note générale : dynamic systems Langues : Anglais (eng) Mots-clés : flexible  structures;  intelligent  robust  controller;  genetic  algorithm;  linear  quadratic  regulator/linear  quadratic  Gaussian  controller  design Résumé : This paper is concerned with the problem of identifying and controlling flexible structures. The structures used exhibit some of the characteristics found in large flexible space structures (LFSSs). Identifying LFSS are problematic in the sense that the modes are of low frequency, lightly damped, and often closely spaced. The proposed identification algorithm utilizes modal contribution coefficients to monitor the data collection. The algorithm is composed of a two-step process, where the input signal for the second step is recomputed based on knowledge gained about the system to be identified. In addition, two different intelligent robust controllers are proposed. In the first controller, optimization is concerned with performance criteria such as rise time, overshoot, control energy, and a robustness measure among others. Optimization is achieved by using an elitism based genetic algorithm (GA). The second controller uses a nested GA resulting in an intelligent linear quadratic regulator/linear quadratic Gaussian (LQR/LQG) controller design. The GAs in this controller are used to find the minimum distance to uncontrollability of a given system and to maximize that minimum distance by finding the optimal coefficients in the weighting matrices of the LQR/LQG controller. The proposed algorithms and controllers are tested numerically and experimentally on a model structure. The results show the effectiveness of the proposed two-step identification algorithm as well as the utilization of GAs applied to the problem of designing optimal robust controllers. DEWEY : 629.8 ISSN : 0022-0434 En ligne : http://dynamicsystems.asmedigitalcollection.asme.org/issue.aspx?journalid=117&is [...] [article] System identification and robust controller design using genetic algorithms for flexible space structures [texte imprimé] / Marco P. Schoen, Auteur ; Randy C. Hoover, Auteur ; Sinchai Chinvorarat, Auteur . - 2009 . - 11 p. dynamic systems Langues : Anglais (eng) in Transactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 131 N° 3 (Mai 2009) . - 11 p. Mots-clés : flexible  structures;  intelligent  robust  controller;  genetic  algorithm;  linear  quadratic  regulator/linear  quadratic  Gaussian  controller  design Résumé : This paper is concerned with the problem of identifying and controlling flexible structures. The structures used exhibit some of the characteristics found in large flexible space structures (LFSSs). Identifying LFSS are problematic in the sense that the modes are of low frequency, lightly damped, and often closely spaced. The proposed identification algorithm utilizes modal contribution coefficients to monitor the data collection. The algorithm is composed of a two-step process, where the input signal for the second step is recomputed based on knowledge gained about the system to be identified. In addition, two different intelligent robust controllers are proposed. In the first controller, optimization is concerned with performance criteria such as rise time, overshoot, control energy, and a robustness measure among others. Optimization is achieved by using an elitism based genetic algorithm (GA). The second controller uses a nested GA resulting in an intelligent linear quadratic regulator/linear quadratic Gaussian (LQR/LQG) controller design. The GAs in this controller are used to find the minimum distance to uncontrollability of a given system and to maximize that minimum distance by finding the optimal coefficients in the weighting matrices of the LQR/LQG controller. The proposed algorithms and controllers are tested numerically and experimentally on a model structure. The results show the effectiveness of the proposed two-step identification algorithm as well as the utilization of GAs applied to the problem of designing optimal robust controllers. DEWEY : 629.8 ISSN : 0022-0434 En ligne : http://dynamicsystems.asmedigitalcollection.asme.org/issue.aspx?journalid=117&is [...]