Ponencia
Nonlinear flight control system with neural networks
Autor/es | Esteban Roncero, Sergio
Balakrishnan, S.N. |
Departamento | Universidad de Sevilla. Departamento de Ingeniería Aeroespacial y Mecánica de Fluidos |
Fecha de publicación | 2001-08-09 |
Fecha de depósito | 2015-09-03 |
Publicado en |
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ISBN/ISSN | 978-156347945-8 |
Resumen | In this study an adaptive critic based neural network controller is developed to obtain near optimal control laws for a nonlinear automatic flight control system. The adaptive critic approach consists of two neural networks. ... In this study an adaptive critic based neural network controller is developed to obtain near optimal control laws for a nonlinear automatic flight control system. The adaptive critic approach consists of two neural networks. The first network, called the critic, captures the mapping between the states of a dynamical system and the co-states that arise in an optimal control problem. The second network, called the action network, maps the states of a system to the control. This study uses nonlinear aircraft models in the stall regions from a paper (Garrad and Jordan2 to develop optimal neural controllers for an aircraft; we then compare the results with singular perturbation based nonlinear controllers developed in the literature. The results show that with the neural controllers the aircraft can operate in a broader region of angles of attack beyond stall as compared to other linear and nonlinear controllers. |
Agencias financiadoras | University of Missouri-Rolla |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Nonlinear_ACNN_AIAA.pdf | 1.566Mb | [PDF] | Ver/ | Artículo principal |