Neural Network Dynamic Surface Backstepping Control for the Speed and Tension System of Reversible Cold Strip Rolling Mill

2017 ◽  
Vol 20 (4) ◽  
pp. 1452-1463 ◽  
Author(s):  
Le Liu ◽  
Yu Han ◽  
Yiming Fang ◽  
Minghao Lin ◽  
Nuan Shao
Keyword(s):  
Neural Network ◽  
Rolling Mill ◽  
Backstepping Control ◽  
Network Dynamic ◽  
Strip Rolling ◽  
Dynamic Surface ◽  
Neural Network Dynamic ◽  
Cold Strip Rolling

scholarly journals Adaptive neural network dynamic surface control for musculoskeletal robots

2014 ◽  
Author(s):  
Michael Jantsch ◽  
Steffen Wittmeier ◽  
Konstantinos Dalamagkidis ◽  
Guido Herrmann ◽  
Alois Knoll
Keyword(s):  
Neural Network ◽  
Dynamic Surface Control ◽  
Network Dynamic ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Surface Control ◽  
Neural Network Dynamic

scholarly journals Neural networks-based robust adaptive flight path tracking control of large transport

2018 ◽  
Vol 38 (3) ◽  
pp. 268-278
Author(s):  
Maolong Lv ◽  
Xiuxia Sun ◽  
G. Z. Xu ◽  
Z. T. Wang
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Nonlinear Modeling ◽  
Dynamic Surface Control ◽  
Continuous Functions ◽  
Network Dynamic ◽  
Dynamic Surface ◽  
The Neural Network ◽  
Neural Network Dynamic ◽  
Robust Adaptive

For the ultralow altitude airdrop decline stage, many factors such as actuator nonlinearity, the uncertain atmospheric disturbances, and model unknown nonlinearity affect the precision of trajectory tracking. A robust adaptive neural network dynamic surface control method is proposed. The neural network is used to approximate unknown nonlinear continuous functions of the model, and a nonlinear robust term is introduced to eliminate the actuator’s nonlinear modeling error and external disturbances. From Lyapunov stability theorem, it is rigorously proved that all the signals in the closed-loop system are bounded. Simulation results confirm the perfect tracking performance and strong robustness of the proposed method.

Observer-based neural network dynamic surface control of single-phase LCL-filtered grid-connected inverters under parametric uncertainties and weak grid condition

2021 ◽  
pp. 014233122110195
Author(s):  
Sajjad Shoja-Majidabad ◽  
Majid Moradi Zirkohi
Keyword(s):  
Neural Network ◽  
Single Phase ◽  
Dynamic Surface Control ◽  
Current Control ◽  
Network Dynamic ◽  
Parametric Uncertainties ◽  
Dynamic Surface ◽  
Weak Grid ◽  
Surface Control ◽  
Neural Network Dynamic

This paper focuses on the current control of single-phase LCL-filtered grid-connected inverters in the presence of parametric uncertainties and weak grid condition. Therefore, a novel neural network dynamic surface control (NNDSC) method is proposed by overcoming the problem of ‘explosion of complexity’. In addition, radial basis function neural networks (RBFNNs) are employed to approximate the system parametric uncertainties. Furthermore, by considering practical considerations, a novel state observer (SO) is designed to estimate the inverter-side current and capacitor voltage. As a result, additional current and voltage sensors are not required, which makes the implementation of the proposed approach straightforward and reliable. The origin neighbourhood convergence of estimated and tracking errors is assured through Lyapunov stability theorem and Young’s inequality. The effectiveness and performance of the proposed NNDSC+SO approach is demonstrated through MATLAB/Simpower simulations in view of the reference current changes, LCL filter parametric uncertainties and weak grid condition.

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