TY - GEN
T1 - Discrete time recurrent neural network sliding mode observer
AU - Salgado, I.
AU - Chairez, I.
AU - García, A.
PY - 2010
Y1 - 2010
N2 - State estimation for uncertain systems affected by external noises is an important problem in control theory. This paper deals with the state observation problem when the dynamic model of a discrete plant contains uncertainties or is partially unknown. The suggested observer is oriented to solve the state observation problem of discrete time nonlinear systems. Most of the existing results using the neural networks approach have been developed for continuous time systems using. The recurrent neural network (RNN) have shown several advantages to treat many different control and state estimation problems. In this paper, it is presented a new discrete-time observer using the structure of a classical RNN. This observer includes a correction term using the output information and the first order sliding modes. The second method Lyapunov is applied to generate a new learning law, that contains an adaptive adjustment rate. This study proofs the stability condition for the free parameters included in the neural-observer. A numerical example is given using the RNN in the estimation of a mathematical model describing the HIV infection, that includes non-infected cells, infected cells and free virions. This model was used to generated the data using to test the observer.
AB - State estimation for uncertain systems affected by external noises is an important problem in control theory. This paper deals with the state observation problem when the dynamic model of a discrete plant contains uncertainties or is partially unknown. The suggested observer is oriented to solve the state observation problem of discrete time nonlinear systems. Most of the existing results using the neural networks approach have been developed for continuous time systems using. The recurrent neural network (RNN) have shown several advantages to treat many different control and state estimation problems. In this paper, it is presented a new discrete-time observer using the structure of a classical RNN. This observer includes a correction term using the output information and the first order sliding modes. The second method Lyapunov is applied to generate a new learning law, that contains an adaptive adjustment rate. This study proofs the stability condition for the free parameters included in the neural-observer. A numerical example is given using the RNN in the estimation of a mathematical model describing the HIV infection, that includes non-infected cells, infected cells and free virions. This model was used to generated the data using to test the observer.
KW - Discrete-time Recurrent Neural Network
KW - State estimation and HIV infection
UR - http://www.scopus.com/inward/record.url?scp=79959401853&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2010.5596883
DO - 10.1109/IJCNN.2010.5596883
M3 - Contribución a la conferencia
AN - SCOPUS:79959401853
SN - 9781424469178
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
Y2 - 18 July 2010 through 23 July 2010
ER -
