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@InProceedings{SilvaRios:2000:NePrSa,
               author = "Silva, Jaime Augusto da Silva and Rios Neto, Atair",
                title = "Neural predictive satellite attitude control based on Kalman 
                         filtering algorithms",
            booktitle = "Proceedings...",
                 year = "2000",
         organization = "International Symposium Space Dynamics.",
             keywords = "ENGENHARIA E TECNOLOGIA ESPACIAL, attitude control, Kalman 
                         filtering.",
             abstract = "An artificial neural network predictive control scheme considered 
                         for satellite attitude control. Kalman filtering algorithms are 
                         used only to train the associated feedforward neural network 
                         modeling the dynamics the plant but to also estimate the control 
                         actions. It is shown that the optimization of a predictive 
                         quadratic performance functional, used to determine the discrete 
                         control actions, can be viewed and treated, in a typical 
                         iteration, as a stochasti optimal linear parameter estimation 
                         problem. The algorithms obtained are shown to be the result of 
                         application of Newton's method to appropriate control optimization 
                         functionals that provide solutions that converge to smooth and 
                         reference tracking controls. The proposed scheme is then applied 
                         to a three-axes satellite attitude control with a double-gimbaled 
                         momentum wheel. Results on simulations and tests for the situation 
                         of fine pointing torques and errors in the initial satellite 
                         attitude show excellent performance of the proposed scheme.",
  conference-location = "Bearritz, FR",
      conference-year = "26-30 June 2000",
                label = "10182",
         organisation = "CNES",
           targetfile = "9261.pdf",
        urlaccessdate = "21 jan. 2021"
}


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