Internal Model Control for Inverse System Based on Support Vector Machine and Its Application

2006 ◽  
Author(s):  
Sheng Liu ◽  
Yanyan Li ◽  
Yanchun Du
Keyword(s):  
Support Vector Machine ◽  
Internal Model ◽  
Internal Model Control ◽  
Inverse System ◽  
Support Vector ◽  
Model Control

Internal Model Control of Three-Phase Four-Leg Active Power Filter Based on Online LS-SVM ANN Inverse System Method

2011 ◽  
Vol 383-390 ◽  
pp. 2132-2137
Author(s):  
Hong Qi ◽  
Zhen Hua Shao
Keyword(s):  
Internal Model ◽  
Active Power Filter ◽  
Coupling Model ◽  
Active Power ◽  
Internal Model Control ◽  
Inverse System ◽  
Control Effect ◽  
Power Filter ◽  
Three Phase ◽  
Model Control

In dealing with the problem of the SAPF’s nonlinear and strong coupling model, the internal model control of three-phase four-leg active power filter based on online ANN method is studied in this paper. With the ANN’s nonlinear mapping ability of self-learning and self-organizing modeling, the inverse system can be approximated by online LS-SVM. In order to have a good linearization control effect, the internal model control based on ANN is proposed for the combined pseudo-linear system. This method can be used to design effective controllers for nonlinear system with unknown mathematical models. At last, the simulation results show that a good steady-state performance can be obtained under the improved method

scholarly journals An Internal Model Controller for Three-Phase APF Based on LS-Extreme Learning Machine

2014 ◽  
Vol 8 (1) ◽  
pp. 717-722
Author(s):  
Zhenhua Shao ◽  
Tianxiang Chen ◽  
Li-an Chen ◽  
Hong Tian
Keyword(s):  
Control System ◽  
Least Squares ◽  
Extreme Learning Machine ◽  
Internal Model ◽  
Internal Model Control ◽  
Support Vector ◽  
Three Phase ◽  
Model Control ◽  
Internal Model Controller ◽  
Learning Machine

Aiming at the problem that the three-phase APF’s dynamic model is a multi-variable, nonlinear and strong coupling system, an internal model controller for three-phase APF based on LS-Extreme Learning Machine is studied in this paper. As a novel single hidden layer feed-forward neural networks, extreme learning machine (ELM) has several advantages: simple net structural, fast learning speed, good generalization performance and so on. In order to improve the controller’s dynamic responses, a least squares extreme learning machine for internal model control is proposed. A least squares ELM regression (LS-ELMR) model for the three-phase APFS on-line monitoring was built from external factors with in-out datum. Moreover, the relative stable error is presented to evaluate the system performance and the features for the internal model control system based on extreme learning machine, neural network, kernel ridge regress and support vector machine. The experimental results show that the LS-internal model control system based on extreme learning machine has good dynamic performance and strong filtering result.

Decoupling control of bearingless permanent magnet synchronous motors with LSSVM inverse system method and internal model controller

2020 ◽  
pp. 1-20
Author(s):  
Ke Li ◽  
Feng Ling ◽  
Xiaodong Sun ◽  
Zebin Yang
Keyword(s):  
Control System ◽  
Permanent Magnet ◽  
Internal Model ◽  
Internal Model Control ◽  
Decoupling Control ◽  
Support Vector ◽  
Inverse Models ◽  
Control Scheme ◽  
Model Control ◽  
Internal Model Controller

In this paper, a novel decoupling control scheme combining least squares support vector machines (LSSVM) inverse models and 2-degree-of-freedom (DOF) internal model controllers is employed in the decoupling control system of the bearingless permanent magnet synchronous motor (BPMSM). This scheme can be used to enhance the control properties of high-precision, fast-response, and strong-robustness for the BPMSM system, and effectively eliminate the nonlinear and coupling influence. It introduces LSSVM inverse models into the original BPMSM system to constitute a decoupled pseudo-linear system. In addition, the particle swarm optimization algorithm (PSO) is used to optimize parameters of the LSSVM, which improves its fitting ability and prediction accuracy. What is more, the internal model control scheme is used to design additional closed-loop controllers, thereby improving the robustness of the entire control system. Therefore, this scheme successfully combines the advantages of the LSSVM inverse models and the internal model controller. It can enhance the stability and the static as well as dynamic properties of the whole BPMSM system while independently adjusting the tracking and interference rejection performances. The effectiveness of the proposed scheme has been verified by simulation results at various operations.

Nonlinear Internal Model Control for Bearingless Induction Motor Based on Inverse System Theory

2014 ◽  
Vol 1037 ◽  
pp. 258-263
Author(s):  
Zheng Qi Wang ◽  
Xue Liang Huang
Keyword(s):  
System Theory ◽  
Induction Motor ◽  
Internal Model ◽  
Control Method ◽  
Internal Model Control ◽  
Inverse System ◽  
Inverse System Method ◽  
Coupling System ◽  
System Method ◽  
Model Control

The bearingless induction motor is a nonlinear, multi-variable and strongly coupling system. In this paper, a new nonlinear internal model control (IMC) strategy based on inverse system theory is proposed to realize the decoupling control for the bearingless induction motor. The mathematical model of the motor is built and then the inverse system method is applied to decouple the original nonlinear system. Finally the internal model control method is introduced to ensure the robustness of the closed-loop system. The effectiveness of the proposed strategy are demonstrated by simulation.

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