Time delay system identification using controlled recurrent neural network and discrete bayesian optimization

2021 ◽  
Author(s):  
Shenyi Ding ◽  
Zhijie Wang ◽  
Jue Zhang ◽  
Fang Han ◽  
Xiaochun Gu
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
System Identification ◽  
Recurrent Neural Network ◽  
Delay System ◽  
Bayesian Optimization ◽  
Time Delay System

scholarly journals The Research of Improved Active Disturbance Rejection Control Algorithm for Particleboard Glue System Based on Neural Network State Observer

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 259
Author(s):  
Peiyu Wang ◽  
Chunrui Zhang ◽  
Liangkuan Zhu ◽  
Chengcheng Wang
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Disturbance Rejection ◽  
Sliding Mode ◽  
Control Object ◽  
State Observer ◽  
Delay System ◽  
Time Delay System ◽  
Network State ◽  
Active Disturbance Rejection

For achieving high-performance control for a particleboard glue mixing and dosing control system, which is a time-delay system in low frequency working conditions, an improved active disturbance rejection controller is proposed. In order to reduce overshoot caused by a given large change between the actual output and expected value of the control object, a tracking differentiator (TD) is used to arrange the appropriate excesses. Through the first-order approximation of the time-delay link, the time-delay system is transformed into an output feedback problem with unknown function. Using the neural network state observer (NNSO), a sliding mode control law is used to achieve the accurate and fast tracking of the output signal. Finally, the numerical simulation results verify the effectiveness and feasibility of the proposed method.

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