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@Article{CaldeiraCarvCost:2017:EsTrVe,
               author = "Caldeira, Ald{\'e}lio Bueno and Carvalho, Michelle Soraia de and 
                         Costa Neto, Ricardo Teixeira da",
          affiliation = "{Instituto Militar de Engenharia (IME)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Militar de Engenharia 
                         (IME)}",
                title = "Estimation of tracked vehicle suspension parameters",
              journal = "Acta Scientiarum Technology",
                 year = "2017",
               volume = "39",
               number = "1",
                pages = "51--57",
                month = "jan./mar.",
             keywords = "vehicle suspension, inverse problem, R2W, PSO.",
             abstract = "This work aims to estimate the suspension stiffness and damping 
                         coefficient of a tracked vehicle by using an inverse problem 
                         technique based on Particle Swarm Optimization ( PSO) and on 
                         Random Restricted Window (R2W). The tracked vehicle has ten road 
                         wheels. Each road wheel is linked to a passive and independent 
                         suspension. A half car model with seven degrees of freedom 
                         describes the bounce and pitch dynamics of the chassis and the 
                         vertical dynamics of the wheels. Bounce and pitch accelerations 
                         are evaluated when the vehicle traverses a bump terrain. The 
                         inverse problem approach minimizes the total quadratic error 
                         between estimated and pseudo-experimental data for bounce and 
                         pitch accelerations. The viability of a field experiment to 
                         estimate the suspension parameters is analyzed, as well as the 
                         performance of the employed optimization methods and the effects 
                         of the noise on pseudo-experimental data.",
                  doi = "10.4025/actascitechnol.v39i1.29385",
                  url = "http://dx.doi.org/10.4025/actascitechnol.v39i1.29385",
                 issn = "1806-2563",
             language = "en",
           targetfile = "caldeira.pdf",
        urlaccessdate = "27 nov. 2020"
}


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