Observador adaptativo baseado em escalonamento e redes neurais
(Adaptive observer design based on scaling and neural networks)
José Alfredo Ruiz Vargas (firstname.lastname@example.org)1, Kevin Herman Muraro Gularte (email@example.com)1, Elder Moreira Hemerly (firstname.lastname@example.org)2
1Universidade de Brasília2Instituto Tecnológico de Aeronáutica
This paper appears in: Revista IEEE América Latina
Publication Date: June 2013
Volume: 11, Issue: 4
Some works based on neural networks have been proposed to estimate adaptively the states of uncertain systems. However, they are subject to several conditions such as previous knowledge of upper bounds for the weight and approximation errors, ideal switching, and previous sample data for an off-line learning phase, which difficult their application. In this paper, an adaptive observer for uncertain nonlinear systems in the presence of disturbances is proposed in order to avoid the above mentioned limitations. Based on a neural Luenberger-like observer, scaling and Lyapunov theory, an adaptive scheme is proposed to make ultimately bounded the on-line observer error. Besides, it is shown that the scaling of unknown nonlinearities, previous to the neural approximation, has a positive impact on performance and application of our algorithm, since it allows the residual state error manipulation without any additional linear matrix inequality solution. To validate the theoretical results, the state estimation of the Rössler oscilator system is performed.
adaptive observers, uncertain systems, neural networks, Lyapunov methods
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