Takagi-Sugeno Fuzzy model and control of a boost converter using Type-I Internal Model Control

2013 ◽  
Author(s):  
Raymundo Cordero Garcia ◽  
Walter I. Suemitsu ◽  
Joao Onofre Pereira Pinto
Keyword(s):  
Internal Model ◽  
Fuzzy Model ◽  
Boost Converter ◽  
Internal Model Control ◽  
Type I ◽  
Model Control ◽  
Takagi Sugeno ◽  
And Control

Two fuzzy internal model control methods for nonlinear uncertain systems

2017 ◽  
Vol 10 (2) ◽  
pp. 223-240 ◽  
Author(s):  
Amira Aydi ◽  
Mohamed Djemel ◽  
Mohamed Chtourou
Keyword(s):  
Internal Model ◽  
Fuzzy Model ◽  
Internal Model Control ◽  
Local Model ◽  
Parametric Uncertainties ◽  
Local Models ◽  
Content Type ◽  
Model Control ◽  
The Stability ◽  
Takagi Sugeno

Purpose The purpose of this paper is to use the internal model control to deal with nonlinear stable systems affected by parametric uncertainties. Design/methodology/approach The dynamics of a considered system are approximated by a Takagi-Sugeno fuzzy model. The parameters of the fuzzy rules premises are determined manually. However, the parameters of the fuzzy rules conclusions are updated using the descent gradient method under inequality constraints in order to ensure the stability of each local model. In fact, without making these constraints the training algorithm can procure one or several unstable local models even if the desired accuracy in the training step is achieved. The considered robust control approach is the internal model. It is synthesized based on the Takagi-Sugeno fuzzy model. Two control strategies are considered. The first one is based on the parallel distribution compensation principle. It consists in associating an internal model control for each local model. However, for the second strategy, the control law is computed based on the global Takagi-Sugeno fuzzy model. Findings According to the simulation results, the stability of all local models is obtained and the proposed fuzzy internal model control approaches ensure robustness against parametric uncertainties. Originality/value This paper introduces a method for the identification of fuzzy model parameters ensuring the stability of all local models. Using the resulting fuzzy model, two fuzzy internal model control designs are presented.

Identification and Control Design of Fuzzy Takagi-Sugeno Model for Pressure Process Rig

2012 ◽  
Vol 605-607 ◽  
pp. 1810-1818
Author(s):  
Aries Subiantoro ◽  
F. Yusivar ◽  
B. Budiardjo ◽  
M.I. Al-Hamid
Keyword(s):  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Internal Model Control ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Model Control ◽  
Low Pass ◽  
Instrumental Variable Method ◽  
Takagi Sugeno ◽  
And Control

The design of an intelligent controller based on fuzzy TS model for a pressure process rig is presented. The proposed controller consists of a fuzzy TS model, a feedback fuzzy TS model, and a low pass filter combined in an internal model control structure. The identification of the fuzzy TS model uses fuzzy clustering technique to mimic the nonlinearity characteristic of the process. Instead of least-squares algorithm, the instrumental variable method is used to estimate the consequent parameters of the fuzzy TS model in order to avoid inconsistency problem. The identified model is validated with the performance indicators variance-accounted-for and root mean square. By using the technique of inverse fuzzy model analytically, the feedback fuzzy controller is designed based on the identified fuzzy TS model. The performance of the proposed controller is verified through experiments at various operating points.

scholarly journals Experimental validation of fractional order internal model controller design on buck and boost converter

2020 ◽  
pp. 002029402092226
Author(s):  
Shivam Jain ◽  
Yogesh V Hote ◽  
Padmalaya Dehuri ◽  
Deeksha Mittal ◽  
Vishwanatha Siddhartha
Keyword(s):  
Fractional Order ◽  
Internal Model ◽  
Experimental Validation ◽  
Boost Converter ◽  
Internal Model Control ◽  
Control Technique ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Control Scheme ◽  
Model Control

In this paper, fractional order internal model control technique is formulated for non-ideal dc–dc buck and boost converter. The fractional order internal model control approach integrates the concept of Commande Robuste d’Ordre Non Entier principle for tuning a fractional order filter with internal model control scheme. The final controller can be expressed as a series combination of proportional integral derivative controller and a fractional order low pass filter. To assess the robustness of the proposed fractional order internal model control scheme, both the servo response and regulatory response of the dc–dc converters are investigated in the presence of disturbances. The efficacy of fractional order internal model control technique is demonstrated via comparison with 2 degrees of freedom internal model control scheme. Furthermore, an experimental validation of fractional order internal model control is conducted on laboratory setup, and a dSPACE 1104 microcontroller is used for hardware implementation. The simulation results and the hardware validation are a testimony to the effectiveness of fractional order internal model control technique.

scholarly journals Sliding Mode Control Based on Internal Model for a Non-minimum phase Buck and Boost Converter

Enfoque UTE ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 41-53
Author(s):  
Byron Cajamarca ◽  
Óscar Camacho Quintero ◽  
Danilo Chávez ◽  
Paulo Leica ◽  
Marcelo Pozo
Keyword(s):  
Sliding Mode Control ◽  
Internal Model ◽  
Sliding Mode ◽  
Pi Controller ◽  
Boost Converter ◽  
Internal Model Control ◽  
Minimum Phase ◽  
Mode Control ◽  
Model Control ◽  
Control Schemes

This work presents the application of different schemes to control a non-minimum phase Buck-Boost converter. Three control schemes are used. The first controller presented is a PI controller, the second one is Sliding Mode Control and the third one is a combination of two control schemes, Internal Model Control and Sliding Mode Control. The controllers are designed from a Right-Half Plane Zero (RHPZ) reduced order model. The RHPZ model is converted, using Taylor approximation, in a First Order Plus Dead Time (FOPDT) model and after that, the controllers are obtained. The performance of the SMC-IMC is compared against to a PI controller and a SMC. The simulation results show that SMC-IMC improves the converter response, reducing the chattering and presenting better robustness for load changes

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