STATE ESTIMATION BASED ON STOCHASTIC AND ZONOTOPIC APPROACHES: PART I - LINEAR SYSTEMS
Alesi Augusto de Paula1; Bruno Otávio Soares Teixeira1; Guilherme Vianna Raffo1
1 Universidade Federal de Minas Gerais
Baixar PDF doi:10.20906/CPS/CBA2018-0637
Resumo
This paper presents a comparative review on the most usual stochastic and zonotopic filtering methods in the literature for state estimation of uncertain linear systems. The mean and the confi dence level of the Gaussian random variable are compared to the center and uncertainty of the zonotopic variable. To achieve that, a unifi ed notation for these approaches is proposed. On one hand, the Kalman filter is an algorithm often used for treating states as Gaussian variables, whose probability density function is simple to represent. On the other hand, the estimation of zonotopic states has owned relevance in the literature due to intrinsic properties of sets, which guarantee inclusion of the exact states into the estimated sets and improved computational burden on the computation of domains.
Palavras-chave: Kalman filter; Zonotopic filter; State estimation; Linear systems