Discrete-time reduced order neural observers for uncertain nonlinear systems.
作者： ALMA Y. ALANIS , EDGAR N. SANCHEZ , LUIS J. RICALDE
DOI: 10.1142/S0129065710002218
关键词: Reduced order 、 Van der Pol oscillator 、 Nonlinear system 、 Discrete time and continuous time 、 Observer (quantum physics) 、 Basis (linear algebra) 、 Extended Kalman filter 、 Control theory 、 Mathematics 、 Artificial neural network
摘要: This paper focusses on a novel discrete-time reduced order neural observer for nonlinear systems, which model is assumed to be unknown. robust in presence of external and internal uncertainties. The proposed scheme based recurrent high network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm, using parallel configuration. work includes the stability proof estimation error basis Lyapunov approach; illustrate applicability, simulation results oscillator are included.
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