Título: Real-Time Control Based On High Order Neural Networks Using Stochastic Estimation
Autores: Castañeda, Carlos E.; Hermosillo, Fidencio C.; Esquivel, P.; Jurado, Francisco
Resumo: An adaptive discrete-time regulator system for a Furuta pendulum is presented. A high order neural network in discrete-time is used to identify the plant behavior; this network is trained with an extended Kalman filter where the associated state and measurement noises discrete-time covariance matrices are calculated with stochastic estimation. Then, the discrete-time block control and sliding mode techniques are used to develop the regulation for the angular position of a Furuta pendulum. Real-time results presented in this paper shows that the proposed method provides accurate estimation for the covariance matrices associated in the extended Kalman filter.
Palavras-chave: High order neural networks; extended Kalman filter; stochastic estimation
Código DOI: 10.21528/CBIC2011-20.1
Artigo em pdf: st_20.1.pdf
Arquivo BibTex: st_20.1.bib