@Article{HollwegEvMaBoTaMo:2023:SeMeAd,
               author = "Hollweg, Guilherme Vieira and Evald, Paulo Jefferson Dias de 
                         Oliveira and Mattos, Everson and Borin, Lucas Cielo and Tambara, 
                         Rodrigo Varella and Montagner, Vinicius Foletto",
          affiliation = "{University of Michigan} and Universidade Federal de Pelotas, 
                         (UFPel) and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Santa Maria (UFSM)} and {Universidade 
                         Federal de Santa Maria (UFSM)} and {Universidade Federal de Santa 
                         Maria (UFSM)}",
                title = "Self-tuning methodology for adaptive controllers based on genetic 
                         algorithms applied for grid-tied power converters",
              journal = "Control Engineering Practice",
                 year = "2023",
               volume = "135",
                pages = "105500",
             keywords = "Optimal parametrization, Genetic algorithm, Robust model reference 
                         adaptive control, Sliding mode control, Super-twisting.",
             abstract = "The performance and stability of adaptive controllers is highly 
                         dependent on its parameter initialization. For non-expert 
                         designers, the parametrization of these controllers can be highly 
                         time consuming and an exhausting task. In order to improve the 
                         response of adaptive controllers without needing more specialized 
                         knowledge, this work proposes a procedure for self-tuning (optimal 
                         parametrization) of direct type adaptive controllers using a 
                         genetic algorithm. The proposal does not harm any property of 
                         stability and convergence of the adaptive strategy, only adding an 
                         off-line stage of parameter selection, which is carried out in a 
                         reasonable computational time. As a case study, this self-tuning 
                         procedure is applied for initialization of nine parameters of a 
                         Robust Model Reference Adaptive Controller and Adaptive 
                         Super-Twisting Sliding Mode, a known adaptive structure from the 
                         literature, suitable for current regulation of three-phase voltage 
                         source converters operating under grid impedance variations. For 
                         comparison with the proposed self-tuning, a similar procedure 
                         using fmincon and simulated annealing algorithms are also tested. 
                         The experimental results show that the proposed procedure using 
                         Genetic Algorithm can provide lower tracking error, faster 
                         regulation dynamics and reduced settling time, leading to better 
                         performance than the same controller with parameters initialized 
                         empirically.",
                  doi = "10.1016/j.conengprac.2023.105500",
                  url = "http://dx.doi.org/10.1016/j.conengprac.2023.105500",
                 issn = "0967-0661",
                label = "lattes: 5932117211446307 3 HollwegOlMaBoTaMo:2023:SeMeAd",
             language = "en",
           targetfile = "1-s2.0-S0967066123000692-main.pdf",
        urlaccessdate = "12 maio 2024"
}