Composite adaptive control with fast convergence for multilayer neural network

2019 ◽  
Vol 29 (13) ◽  
pp. 4454-4471 ◽  
Author(s):  
Tao Jiang ◽  
Defu Lin ◽  
Tao Song
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Fast Convergence ◽  
Composite Adaptive Control

Model free adaptive control scheme based on Elman neural network prediction

2020 ◽  
Author(s):  
Yong-wen LIAO ◽  
Yi-ming MA ◽  
Bao-wei CHEN ◽  
Geng-da LI ◽  
De-liang ZENG
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Elman Neural Network ◽  
Model Free ◽  
Control Scheme ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Adaptive Control Scheme

Neural Network Based Adaptive Control of Uncertain and Unknown Nonlinear Systems

2001 ◽  
Author(s):  
Anthony J. Calise
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Nonlinear Systems

scholarly journals Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3389
Author(s):  
Marcin Kamiński ◽  
Krzysztof Szabat
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Dynamic Properties ◽  
Experimental Studies ◽  
Drive System ◽  
Particle Swarm Optimizer ◽  
The Neural Network ◽  
Control Concept ◽  
Constant Coefficients ◽  
Selection Of

This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

Composite adaptive control based on historical data for electro-hydraulic system driven by dual servo motor-pump with unknown disturbance

2021 ◽  
pp. 095965182199860
Author(s):  
Xiaofu Zhang ◽  
Guanglin Shi
Keyword(s):  
Adaptive Control ◽  
Hydraulic System ◽  
Historical Data ◽  
Tracking Performance ◽  
Load Force ◽  
Position Tracking ◽  
Servo Motor ◽  
Estimated Parameters ◽  
Unknown Disturbance ◽  
Composite Adaptive Control

This article presents a composite adaptive control method to improve the position-tracking performance of an electro-hydraulic system driven by dual constant displacement pump and dual servo motor named as a novel electro-hydraulic system with unknown disturbance. A composite adaptive controller based on backstepping method is designed to estimate the uncertainties of electro-hydraulic control system, including the damping coefficient and elastic modulus. In order to release the persistent excitation condition of conventional adaptive control, which is often infeasible in practice, a prediction error based on the online historical data is used to update the estimated parameters. Furthermore, a disturbance observer is used to estimate the disturbance including the unmeasurable load force, friction and other unmodeled disturbance. The experiment results are provided and compared with other methods to verify the effectiveness of the proposed method, and the results have indicated that the proposed method has a better position-tracking performance with the convergent estimated parameters.

Real-Time Modular Deep Neural Network-Based Adaptive Control of Nonlinear Systems

2021 ◽  
pp. 1-1
Author(s):  
Duc M. Le ◽  
Max L. Greene ◽  
Wanjiku A. Makumi ◽  
Warren E. Dixon
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Real Time ◽  
Deep Neural Network

scholarly journals Laguerre Neural Network Driven Adaptive Control of DC-DC Step Down Converter

2020 ◽  
Vol 53 (2) ◽  
pp. 13396-13401
Author(s):  
Tousif Khan Nizami ◽  
Arghya Chakravarty
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Step Down

Neural Network Adaptive Control for Nonlinear Nonnegative Dynamical Systems

2005 ◽  
Vol 16 (2) ◽  
pp. 399-413 ◽  
Author(s):  
T. Hayakawa ◽  
W.M. Haddad ◽  
N. Hovakimyan ◽  
V. Chellaboina
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Dynamical Systems
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