Master-Slave Synchronization for Trajectory Tracking Error Using Time-Delay Recurrent Neural Networks via Krasovskii-Lur’e Functional for Chua’s Circuit

2016 ◽  
Author(s):  
Joel Perez Padron ◽  
Jose Paz Perez Padron ◽  
Angel Flores Hernandez ◽  
Santiago Arroyo
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Trajectory Tracking ◽  
Recurrent Neural Networks ◽  
Tracking Error ◽  
Chua’S Circuit ◽  
Chua's Circuit

scholarly journals Trajectory tracking error using fractional order time-delay recurrent neural networks using Krasovskii-Lur’e functional for Chua’s circuit via inverse optimal control

2019 ◽  
Vol 66 (1) ◽  
pp. 98
Author(s):  
J. Perez Padrón ◽  
J.P. Pérez Padrón ◽  
C.F. Mendez-Barrios ◽  
E.J. Gonzalez-Galvan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimal Control ◽  
Time Delay ◽  
Fractional Order ◽  
Trajectory Tracking ◽  
Recurrent Neural Networks ◽  
Chaos Synchronization ◽  
Tracking Error ◽  
Inverse Optimal Control

This paper presents an application of a Fractional Order Time Delay Neural Networks to chaos synchronization. The two main methodologies, on which the approach is based, are fractional order time-delay recurrent neural networks and the fractional order  inverse optimal control for nonlinear systems. The problem of trajectory tracking is studied, based on the fractional order Lyapunov-Krasovskii and Lur’e theory, that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a reference function is obtained. The method is illustrated for the synchronization, the analytic results we present a trajectory tracking simulation of a fractional order time-delay dynamical network and the Fractional Order Chua’s circuits

Learning of Chua's circuit attractors by locally recurrent neural networks

2001 ◽  
Vol 12 (11) ◽  
pp. 2109-2115 ◽  
Author(s):  
Barbara Cannas ◽  
Silvano Cincotti ◽  
Michele Marchesi ◽  
Fabrizio Pilo
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Chua’S Circuit ◽  
Chua's Circuit ◽  
Locally Recurrent

Long-lead seasonal rainfall forecasting using time-delay recurrent neural networks: a case study

2007 ◽  
Vol 22 (2) ◽  
pp. 229-241 ◽  
Author(s):  
Mohammad Karamouz ◽  
Saman Razavi ◽  
Shahab Araghinejad
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Recurrent Neural Networks ◽  
Seasonal Rainfall ◽  
Rainfall Forecasting

Rapid iterative learning algorithm of nonlinear time-delay system with initial deviation

2020 ◽  
pp. 002072092094057
Author(s):  
Xiaoyan Cheng ◽  
Hongbin Wang ◽  
Qinzhao Wang ◽  
Shaochan Feng
Keyword(s):  
Time Delay ◽  
Trajectory Tracking ◽  
Learning Algorithm ◽  
Tracking Error ◽  
Iterative Learning ◽  
Forgetting Factor ◽  
Initial State ◽  
Trajectory Tracking Control ◽  
Joint Rotation ◽  
Variable Forgetting Factor

A rapid iterative learning control algorithm with variable forgetting factor is applied for a class of nonlinear system with initial error and time-delay. This algorithm eliminats the limitation that the initial state should be reset to the expected one or fixed value at the start of iteration in the learning process of conventional algorithms. The error and the differences between two adjacent error is adopted to correct the controller avoiding the unstable influence of the derivative for PD type algorithm and the available information is fully used to increase convergence rate. Furthermore variable forgetting factor introduced guaranteed a fast convergence of trajectory tracking error Then, with applying the rapid algorithm to the trajectory tracking control of manipulator, the learning speed and tracking performance are both greatly improved. Meanwhile, the control strategy is proposed for the limitation of each joint rotation. The convergence of the method is also proved theoretically. Finally, simulation results illustrates the effectiveness and the real-time ability of the proposed way.

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