A Novel Time-Delay Recurrent Neural Network and Application for Identifying and Controlling Nonlinear Systems

2007 ◽  
Author(s):  
Hongwei Ge ◽  
Wenli Du ◽  
Feng Qian ◽  
Yanchun Liang
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Nonlinear Systems ◽  
Recurrent Neural Network

Identification and control of nonlinear systems by a time-delay recurrent neural network

2009 ◽  
Vol 72 (13-15) ◽  
pp. 2857-2864 ◽  
Author(s):  
Hong-Wei Ge ◽  
Wen-Li Du ◽  
Feng Qian ◽  
Yan-Chun Liang
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Nonlinear Systems ◽  
Recurrent Neural Network ◽  
And Control

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.

scholarly journals Stability and Bifurcation Analysis of a Three-Dimensional Recurrent Neural Network with Time Delay

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Yingguo Li
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Recurrent Neural Network ◽  
Center Manifold ◽  
Dynamical Behavior ◽  
Three Dimensional ◽  
Nonlinear Dynamical ◽  
Bifurcation Direction ◽  
The Stability ◽  
Manifold Theory

We consider the nonlinear dynamical behavior of a three-dimensional recurrent neural network with time delay. By choosing the time delay as a bifurcation parameter, we prove that Hopf bifurcation occurs when the delay passes through a sequence of critical values. Applying the nor- mal form method and center manifold theory, we obtain some local bifurcation results and derive formulas for determining the bifurcation direction and the stability of the bifurcated periodic solution. Some numerical examples are also presented to verify the theoretical analysis.

Multifunctional Electrical Prosthetic Hand -Development of Tendon-driven Mechanism and Controller

2002 ◽  
Vol 14 (6) ◽  
pp. 557-564 ◽  
Author(s):  
Wenwei Yu ◽  
◽  
Daisuke Nishikawa ◽  
Yasuhiro Ishikawa ◽  
Hiroshi Yokoi ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Joint Torque ◽  
Prosthetic Hand ◽  
Neural Network Models ◽  
Nonlinear Properties ◽  
The Stability

The purpose of this research was to develop a tendondriven electrical prosthetic hand, which is characterized by its mechanical torque-velocity converter and a mechanism that can assist proximal joint torque by distal actuators. To cope with time-delay and nonlinear properties of the prosthetic hand, a controller based on a Jordan network, recurrent neural network models, is proposed. The results of experiments on the stability of the controller are confirmed when tracking static wire tensions. Finally, the next prototype of prosthetic hand based on these methods is introduced.

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