Further result on a dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems

Automatica ◽  
2005 ◽  
Vol 41 (12) ◽  
pp. 2161-2162 ◽  
Author(s):  
S.N. Huang ◽  
K.K. Tan ◽  
T.H. Lee
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Recurrent Neural Network ◽  
Adaptive Observer

A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems

Automatica ◽  
1997 ◽  
Vol 33 (8) ◽  
pp. 1539-1543 ◽  
Author(s):  
Young H. Kim ◽  
Frank L. Lewis ◽  
Chaouki T. Abdallah
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Recurrent Neural Network ◽  
Adaptive Observer

Diagonal recurrent neural network observer-based adaptive control for unknown nonlinear systems

2020 ◽  
Vol 42 (15) ◽  
pp. 2833-2856
Author(s):  
Ahmed Elkenawy ◽  
Ahmad M El-Nagar ◽  
Mohammad El-Bardini ◽  
Nabila M El-Rabaie
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Lyapunov Stability Theory ◽  
Adaptive Dynamic Programming ◽  
External Disturbances ◽  
System States

This paper proposes an observer-based adaptive control for unknown nonlinear systems using an adaptive dynamic programming (ADP) algorithm. First, a diagonal recurrent neural network (DRNN) observer is proposed to estimate the unknown dynamics of the nonlinear system states. The proposed neural network offers a simpler structure with deeper memory and guarantees the faster convergence. Second, a neural controller is constructed via ADP method using the observed states to get the optimal control. The optimal control law is determined based on the new structure of the critic network, which is performed using the DRNN. The learning algorithm for the proposed DRNN observer-based adaptive control is developed based on the Lyapunov stability theory. Simulation results and hardware-in-the-loop results indicate the robustness of the proposed ADP to respond the system uncertainties and external disturbances compared with other existing schemes.

A DISCRETE-TIME RECURRENT NEUROFUZZY NETWORK FOR BLACK-BOX MODELING OF INSULIN DYNAMICS IN DIABETIC TYPE-1 PATIENTS

2010 ◽  
Vol 20 (02) ◽  
pp. 149-158 ◽  
Author(s):  
MARCOS A. GONZÁLEZ-OLVERA ◽  
ANA G. GALLARDO-HERNÁNDEZ ◽  
YU TANG ◽  
MARIA CRISTINA REVILLA-MONSALVE ◽  
SERGIO ISLAS-ANDRADE
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Blood Glucose Concentration ◽  
Blood Stream ◽  
Adaptive Observer ◽  
Insulin Concentration ◽  
On Line ◽  
Data Driven Modeling ◽  
Lyapunov Sense

In this work we present a data-driven modeling of the insulin dynamics in different in silico patients using a recurrent neural network with output feedback. The inputs for the identification is the rate of insulin (μU / dl / min) applied to the patient, and blood glucose concentration. The output is insulin concentration (μU / ml) present in the blood stream. Once completed the off-line modeling, this model could be used for on-line monitoring of the insulin concentration for a better treatment. The learning law of the recurrent neural network is inspired by adaptive observer theory, and proven to be convergent in the parameters and stable in the Lyapunov sense, even with only 13 samples available. Simulation results are shown to validate the presented modeling.

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