• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.308-315
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• 2016

This paper addresses a formation control problem for a phugoid model-based multi-agent system in discrete time by using a Takagi-Sugeno (T-S) fuzzy model-based controller design technique. The concerned discrete-time model is obtained by Euler's method. A T-S fuzzy model is constructed through a feedback linearization. A fuzzy controller is then designed to stabilize the T-S fuzzy model. Design condition is presented in the linear matrix inequality format.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.129-132
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• 2001

In this paper, in order to control uncertain missile autopilot, an adaptive fuzzy control(AEC) scheme via parallel distributed compensation(PDC) is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno(T-S) fuzzy model. Moreover adaptive law is designed so that the plant output tracks the stable reference model(SRM), From the simulations results, we can conclude that the suggested scheme can effectively solve the control problems of uncertain missile systems based on T-S fuzzy model.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.108-110
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• 2006

This paper deals with a tracking problem for nonlinear systems using its T-S fuzzy model and internal model. We extend the internal model of linear systems to an internal model of T-S fuzzy systems to accompany with state error of zero. A sufficient condition of the existence of a tracking controller for T-S fuzzy systems is expressed by linear matrix inequalities. A system of inverted pendulum on cart is illustrated to verify our method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.973-983
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• 2015

To improve the control performance of the IPMSM, a novel nonlinear disturbance observer is proposed by using the T-S fuzzy model. A T-S fuzzy model is the combination of local linear models considered at each operating point. Usually the inverse model is easy to obtain in linear systems but not in nonlinear systems. To design a nonlinear disturbance observer, a nonlinear inverse model is obtained based on nonlinear inverse model which is the fuzzy combination of the local linear inverse models. The proposed DOB is used with a PDC controller which is one of the T-S fuzzy controller, and its performance improvement is shown from the simulation results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.419-422
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• 2005

In this note, the Takagi-Sugeno (T-S) fuzzy-model-based state estimator using standard Kalman filter theory is investigated. In that case, the dynamic system model is represented the T-S fuzzy model with the fuzzy state estimation. The steady state solutions can be found for proposed modeling method and dynamic system for maneuvering targets can be approximated as locally linear system. And then, modeled filter is corrected by the fuzzy gain which is a fuzzy system using the relation between the filter residual and its variation. This paper studies the T-S fuzzy model-based state estimator which the dynamic system can be approximated as linear system.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.4
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• pp.391-397
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• 2011

Control of interior permanent magnet (IPMSM) is difficult because its nonlinearity and parameter uncertainty. In this paper, a fuzzy c-regression models clustering algorithm which is based on T-S fuzzy is used to model IPMSM with a series linear model and weight them by memberships. Lagrangian of constrained function is built for calculating clustering centers where training output data are considered. Based on these clustering centers, least square method is applied for T-S fuzzy linear model parameters. As a result, IPMSM can be modeled as T-S fuzzy model for T-S fuzzy control of them.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.4
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• pp.457-465
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• 2010

In this study, a Taylor series (T-S) model based on the Arrhenius, McVetty, and Monkman-Grant equations was developed using a mathematical analysis. In order to reduce fitting errors, the McVetty equation was transformed by considering the first three terms of the Taylor series equation. The model parameters were accurately determined by a statistical technique of maximum likelihood estimation, and this model was applied to the creep data of alloy 617. The T-S model results showed better agreement with the experimental data than other models such as the Eno, exponential, and L-M models. In particular, the T-S model was converted into an isothermal Taylor series (IT-S) model that can predict the creep strength at a given temperature. It was identified that the estimations obtained using the converted ITS model was better than that obtained using the T-S model for predicting the long-term creep life of alloy 617.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.8
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• pp.771-775
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• 2001

In this paper, in order to control uncertain missile autopilot, an adaptive fuzzy control(AFC) scheme via parallel distributed compensation(PDC) is developed for the multi-input/multi -output plants represented by the Takagi-Sugeno(T-S) fuzzy model. Moreover adaptive law is designed so that the plant output tracks the stable reference model(SRM). From the simulations results, we can conclude that the suggested scheme can effectively solve the control problems of uncertain missile systems based on T-S fuzzy model.

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.702-708
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• 2010

This paper proposes a novel linearization method for Takagi.sugeno (TS) fuzzy model. A T-S fuzzy controller consists of linear controllers based on local linear models and the local linear controllers cannot be designed independently because of overall stability conditions which are usually conservative. To use linear control theories easily for T-S fuzzy system, the linearization of T-S fuzzy model is required. However, The linearization of T-S fuzzy model is difficult to be achieved by using existing linearization methods because fuzzy rules and membership functions are included in T-S fuzzy models. So, a new linearization method is proposed for the T-S fuzzy system based on the idea of T-S fuzzy state transformation. For the T-S fuzzy system linearized with uncertainties, a robust optimal controller with the robustness of sliding model control(SMC) is designed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.123-128
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• 1998

This paper proposes design of LFT-based fuzzy controllers for model-following, which are better than the previous input-output linearization controllers, which are not able to follow the model system states and which do not guarantee the stability of all states. The method proposed in this paper provides a LFT-based Takagi-Sugeno(T-S) fuzzy controller with guaranteed stability and model-following via the following steps: First, using LFT(Linear Fractional Transformation) and T-S fuzzy model, controllers, are obtained. Next, error dynamics are obtained for model-following, and errors go to 0(zero). Finally, a T-s fuzzy controller that can stabilizxe the system with the requirement on the control input satisfied is obtained by solving the LMIs with the MATLAB LMI Control Toolbox and a model-following controller is obtained. Simulations are performed for the LFT-based T-S fuzzy controller designed by the proposed method, which show better performance than the results of input-out ut linearization controller.