Multivariable Adaptive Control of Nonlinear Unknown Dynamic Systems Using Recurrent Neural-Network

2006 ◽  
Author(s):  
Chih-Lyang Hwang
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Recurrent Neural Network ◽  
Dynamic Systems

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 Recurrent Neural Network for Hierarchical Control of Interconnected Dynamic Systems

2007 ◽  
Vol 18 (2) ◽  
pp. 466-481 ◽  
Author(s):  
Zeng-Guang Hou ◽  
Madan M. Gupta ◽  
Peter N. Nikiforuk ◽  
Min Tan ◽  
Long Cheng
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Dynamic Systems ◽  
Hierarchical Control
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