Adaptive Inverse Control of Hydro Electric Unit Based on Wavelet Neural Networks

2012 ◽  
Vol 591-593 ◽  
pp. 1200-1203
Author(s):  
Zhong Liao ◽  
Bin Yuan Ye
Keyword(s):  
Neural Networks ◽  
Control Method ◽  
Phase System ◽  
Minimum Phase ◽  
Wavelet Neural Networks ◽  
Adaptive Inverse Control ◽  
New Approach ◽  
Inverse Control ◽  
Unit System ◽  
Phase Systems

Due to the nonlinear, time-variable and non-minimum phase character of hydro electric unit system integrated with water, motor and power, a new adaptive inverse control method of hydro electric unit based on the function approximation ability of the wavelet analysis and the learning characteristic of neural network is presented. The algorithm and formulas and method of adaptive inverse control is studied. It approximates the model and its inversion of hydro electric unit by wavelet neural networks(WNN), and then through constructing an aim function of broad sense, which is effective to the nonlinear non-minimum phase system. Theory and simulation to for hydro electric unit system demonstrate that the control strategy can more effective improve the dynamic and stationary performance than those based on neural networks. It gives a new approach in control for hydro electric unit system besides offer a beneficial reference to the control of non-minimum phase systems.

Decoupling Control for Bearingless Synchronous Reluctance Motor Based on Neural Networks Inverse

2012 ◽  
Vol 150 ◽  
pp. 30-35
Author(s):  
Ze Bin Yang ◽  
Huang Qiu Zhu ◽  
Xiao Dong Sun ◽  
Tao Zhang
Keyword(s):  
Neural Networks ◽  
Control Method ◽  
Decoupling Control ◽  
Performance Simulation ◽  
Inverse Control ◽  
Linear Control Theory ◽  
Synchronous Reluctance Motor ◽  
Reluctance Motor ◽  
Static Performance ◽  
Simulation And Experiment

A novel decoupling control method based on neural networks inverse system is presented in this paper for a bearingless synchronous reluctance motor (BSRM) possessing the characteristics of multi-input-multi-output, nonlinearity, and strong coupling. The dynamic mathematical models are built, which are verified to be invertible. A controller based on neural network inverse is designed, which decouples the original nonlinear system to two linear position subsystems and an angular velocity subsystem. Furthermore, the linear control theory is applied to closed-loop synthesis to meet the desired performance. Simulation and experiment results show that the presented neural networks inverse control strategy can realize the dynamic decoupling of BSRM, and that the control system has fine dynamic and static performance.

scholarly journals Adaptive Inverse Control Based on Kriging Algorithm and Lyapunov Theory of Crawler Electromechanical System

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Guanyu Zhang ◽  
Yitian Wang ◽  
Yiyao Fan ◽  
Chen Chen
Keyword(s):  
Electromechanical Coupling ◽  
Control Method ◽  
Coupling Model ◽  
Virtual Prototype ◽  
Lyapunov Theory ◽  
Electromechanical System ◽  
Prototype Model ◽  
Control Effect ◽  
Adaptive Inverse Control ◽  
Inverse Control

The electromechanical system of a crawler is a multi-input, multioutput strongly coupled nonlinear system. In this study, an adaptive inverse control method based on kriging algorithm and Lyapunov theory is proposed to improve control accuracy during adaptive driving. The electromechanical coupling model of the electromechanical system is established on the basis of the dynamic analysis of the crawler. In accordance with the kriging algorithm, the inverse model of the electromechanical system of the crawler is established by offline data. The adaptive travel control law of the crawler is obtained on the basis of Lyapunov theory. Combined with the kriging algorithm, the adaptive driving reverse control method is designed, and the online system is used to update and perfect the inverse system model in real time. Finally, the virtual prototype model of the crawler is established, and the control effect of the adaptive inverse control method is verified by theoretical analysis and virtual prototype simulation.

Adaptive Inverse Fuzzy Decoupling Control of Bearingless Asynchronous Motor

2014 ◽  
Vol 703 ◽  
pp. 327-330
Author(s):  
Jian Dong Sun ◽  
Yu Xin Sun ◽  
Huang Qiu Zhu ◽  
Xian Xing Liu
Keyword(s):  
Control Method ◽  
Asynchronous Motor ◽  
Poor Performance ◽  
Decoupling Control ◽  
Matlab Simulation ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Highly Nonlinear ◽  
Static Performance ◽  
Control Of Linear Systems

The traditional control has good performance in the control of linear systems while has poor performance in the control of nonlinear systems. The bearingless asynchronous motor is a multivariable nonlinear system with high coupling. In this paper, the method of adaptive inverse control is proposed for these reasons. Firstly, the mathematical model of the bearingless asynchronous motor is built, and the possibility of the existence of the bearingless asynchronous motor system inverse model is explored. Secondly, since the object to be controlled is highly nonlinear and has high variability. In this paper, adaptive inverse fuzzy decoupling control is used to make up the deficiency of traditional adaptive inverse control. Finally, the Matlab simulation model is established. The simulation results show that the control method has good dynamic and static performance.

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