Modified internal model control of induction motor variable frequency speed control system in v/f mode based on neural network generalized inverse

2010 ◽  
Author(s):  
Guoh ◽  
Xiao Xiao ◽  
Chenglong Teng ◽  
Guanxue Yang ◽  
Yan Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Induction Motor ◽  
Internal Model ◽  
Generalized Inverse ◽  
Speed Control ◽  
Internal Model Control ◽  
Variable Frequency ◽  
Model Control ◽  
Speed Control System

Speed Control System for Variable Speed Fixed-Pitch Wind Turbine Based on Internal Model Control

2015 ◽  
Vol 719-720 ◽  
pp. 330-335
Author(s):  
Gan Sheng Zhang ◽  
Wei Xie ◽  
Xiang Jun Li ◽  
Bo Li
Keyword(s):  
Control System ◽  
Wind Turbine ◽  
Internal Model ◽  
External Disturbance ◽  
Speed Control ◽  
Internal Model Control ◽  
Variable Speed ◽  
Model Control ◽  
Speed Control System ◽  
Fixed Pitch

The paper deals with speed control system design for variable speed fixed-pitch (VS-FP) wind turbine based on improved internal model control. The design procedure includes two steps: first nominal controller is considered that the VS-PF wind turbine works well around its operating point; second, a stable compensator is designed to reject the influence of external disturbance on the control performance when external disturbance occurs. Finally, a numerical simulation is presented to illustrate the efficiency of the proposed method.

scholarly journals Research of Smart Car’s Speed Control Based on the Internal Model Control

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Han Yu ◽  
Hamid Reza Karimi ◽  
Xuemei Zhu
Keyword(s):  
Control System ◽  
Internal Control ◽  
Internal Model ◽  
Control Method ◽  
Good Control ◽  
Speed Control ◽  
Internal Model Control ◽  
Determine Parameter ◽  
Model Control ◽  
Speed Control System

Based on analyzing principle about the smart car’s speed control system, the system mathematical model is built. Considering the control optimization, a novel control scheme is proposed based on internal model control, and the internal model controller of speed control system is established. Regarding this subject, the internal control theory is introduced to verify the control performance; the traditional PID control method is employed in the experiment. The experiment indicates that the proposed method based on internal model control is easier to determine parameter and has a well robust and good control result of smart car’s speed.

scholarly journals New Smith Internal Model Control of Two-Motor Drive System Based on Neural Network Generalized Inverse

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Guohai Liu ◽  
Jun Yuan ◽  
Wenxiang Zhao ◽  
Yaojie Mi
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Internal Model ◽  
Generalized Inverse ◽  
Control Structure ◽  
Control Method ◽  
Internal Model Control ◽  
Drive System ◽  
Motor Drive ◽  
Model Control

Multimotor drive system is widely applied in industrial control system. Considering the characteristics of multi-input multioutput, nonlinear, strong-coupling, and time-varying delay in two-motor drive systems, this paper proposes a new Smith internal model (SIM) control method, which is based on neural network generalized inverse (NNGI). This control strategy adopts the NNGI system to settle the decoupling issue and utilizes the SIM control structure to solve the delay problem. The NNGI method can decouple the original system into several composite pseudolinear subsystems and also complete the pole-zero allocation of subsystems. Furthermore, based on the precise model of pseudolinear system, the proposed SIM control structure is used to compensate the network delay and enhance the interference resisting the ability of the whole system. Both simulation and experimental results are given, verifying that the proposed control strategy can effectively solve the decoupling problem and exhibits the strong robustness to load impact disturbance at various operations.

The Application of PSO-BP Neural Network PID Controller in Variable Frequency Speed Regulation System

2014 ◽  
Vol 599-601 ◽  
pp. 1090-1093 ◽  
Author(s):  
Chun Hua Li ◽  
Shao Xiong Xu ◽  
Yang Xie ◽  
Jie Zhao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Speed Control ◽  
Regulation System ◽  
Particle Swarm Algorithm ◽  
Variable Frequency ◽  
Speed Control System ◽  
Neural Network Pid

Variable frequency speed control system hold good stability,more efficient,more energy conservation etc, so it has been widely used in the industrial areas,but the control strategy of traditional was difficult to achieve the desired control effect.This paper adopt particle swarm algorithm and BP neural network to construct the PID controller of PSO-BP neural network , the M-Files of PSO-BP neural network PID based on MATLAB through S-Function, and the mode of PSO-BP neural network PID variable frequency speed control system was established in SIMULINK platform.Simulation results show that the controller hold well robustness, follow and stability,and the dynamic characteristics of the original system was improved, the application value of this method in the variable frequency speed control system was proved.

scholarly journals Permeate Flux Control in SMBR System by Using Neural Network Internal Model Control

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1672
Author(s):  
Norhaliza Abdul Wahab ◽  
Nurazizah Mahmod ◽  
Ramon Vilanova
Keyword(s):  
Neural Network ◽  
Control System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Membrane Fouling ◽  
Internal Model ◽  
Closed Loop ◽  
Internal Model Control ◽  
Permeate Flux ◽  
Model Control

This paper presents a design of a data-driven-based neural network internal model control for a submerged membrane bioreactor (SMBR) with hollow fiber for microfiltration. The experiment design is performed for measurement of physical parameters from an actuator input (permeate pump voltage), which gives the information (outputs) of permeate flux and trans-membrane pressure (TMP). The palm oil mill effluent is used as an influent preparation to depict fouling phenomenon in the membrane filtration process. From the experiment, membrane fouling potential is observed from flux decline pattern, with a rapid increment of TMP (above 200 mbar). Membrane fouling is a complex process and the available models in literature are not designed for control system (filtration performance). Therefore, this work proposes an aeration fouling control strategy to measure the filtration performance. The artificial neural networks (Feed-Forward Neural Network—FFNN, Radial Basis Function Neural Network—RBFNN and Nonlinear Autoregressive Exogenous Neural Network—NARXNN) are used to model dynamic behaviour of flux and TMP. In this case, only flux is used in closed loop control application, whereby the TMP effect is used for monitoring. The simulation results show that reliable prediction of membrane fouling potential is obtained. It can be observed that almost all the artificial neural network (ANN) models have similar shape with the actual data set, with the highest accuracy of more than 90% for both RBFNN and NARXN. The RBFNN is preferable due to simple structure of the network. In the control system, the RBFNN IMC depicts the highest closed loop performance with only 3.75 s (settling time) for setpoint changes when compared with other controllers. In addition, it showed fast performance in disturbance rejection with less overshoot. In conclusion, among the different neural network tested configurations the one based on radial basis function provides the best performance with respect to prediction as well as control performance.

scholarly journals Research on Induction Motor Vector Control System Based on Internal Model Decoupling

2018 ◽  
Vol 232 ◽  
pp. 04029
Author(s):  
Hu-cheng He ◽  
Wen-ting Wang ◽  
Qun Zhu ◽  
Lei Shi
Keyword(s):  
Control System ◽  
Vector Control ◽  
Induction Motor ◽  
Internal Model ◽  
Frequency Control ◽  
Internal Model Control ◽  
Field Orientation ◽  
Stator Current ◽  
Model Control ◽  
Vector Control System

As a high-performance variable frequency control technology, vector control has been widely used in the field of AC speed regulation. However, the cross-coupling potential of the induction motor after the vector transformation still affects the system performance. Therefore, the method is studied in which stator current is decoupled to excitation component and torque component using internal model control, and the internal model decoupling stator current controller is designed based on rotor field orientation. The simulation model of induction motor vector control system based on internal model decoupling is constructed with Matlab/Simulink. The simulation result shows that the internal model controller is superior to the traditional PI controller in disturbance-rejection performance and robustness.

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