• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.496-498
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• 2005

This paper presents intelligent digital redesign method of global approach for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent the complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated linear operators to be matched. Also by using the power series, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs).

• Journal of Mechanical Science and Technology
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• v.18 no.3
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• pp.337-346
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• 2004

The control scheme using fuzzy modeling and Parallel Distributed Compensation (PDC) concept is proposed to provide asymptotic tracking of a reference signal for the flexible joint manipulators with uncertain parameters. From Lyapunov stability analysis and simulation results, the developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop multi-input/multi-output system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.130-135
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• 2004

This paper presents a new design methods of the short-term load forecasting system (STLFS) using the data mining. The structure of the proposed STLFS is divided into two parts: the Takagi-Sugeno (T-S) fuzzy model-based classifier and predictor The proposed classifier is composed of the Gaussian fuzzy sets in the premise part and the linearized Bayesian classifier in the consequent part. The related parameters of the classifier are easily obtained from the statistic information of the training set. The proposed predictor takes form of the convex combination of the linear time series predictors for each inputs. The problem of estimating the consequent parameters is formulated by the convex optimization problem, which is to minimize the norm distance between the real load and the output of the linear time series estimator. The problem of estimating the premise parameters is to find the parameter value minimizing the error between the real load and the overall output. Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.185-190
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• 2009

In this paper, an observer-based intelligent controller for the nonlinear networked control systems with packet loss is proposed for wireless sensor network. For the intelligent control of the nonlinear system, it uses the fuzzy system with Takagi-Sugeno (T-S) fuzzy model. The observer is designed for the fuzzy networked control system, and the output feedback controller is proposed for the stability of estimates and errors. The stability condition of the closed-loop system with the proposed controller is represented to the linear matrix inequality (LMI) form, and the observer and control gain are obtained by LMI. An example is given to show the verification discussed throughout the paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.700-705
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• 2012

This paper addresses an intelligent digital redesign (IDR) technique for observer-based output-feedback control systems, in order to efficiently convert a pre-designed Takagi-Sugeno fuzzy model-based analog controller into a sampled-data one in the sense of state matching. A delta operator is used to get an asymptotic relation between the analog and the sampled-data control systems. The IDR problem is viewed as a minimization problem of the norm distances between linear operator to be matched. The condition is represented as linear matrix inequalities, and the separation principle on the IDR is shown.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.559-564
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• 2002

This Paper Proposes a design method of a fuzzy output feedback controller for the nonlinear systems with the unknown time- delay. Recently, Cao et ai. proposed a stabilization method for the nonlinear time-delay systems using a fuzzy controller when the time-delay is known. However, the time-delay is likely to be unknown in practical. We represent the nonlinear systems with the unknown time-delay by Takagi-Sugeno (T-5) fuzzy model and design the fuzzy observer and the parallel distributed compensation (PDC) law based on this observer. By applying Lyapunov-Krasovskii theorem to the closed-loop system, the sufficient condition for the asymptotic stability of the equilibrium Point is derived and converted into the linear matrix inequality (LMI) Problem.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.70-70
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• 2000

Recently, a neuro-fuzzy approach, a combination of neural networks and fuzzy reasoning, has been playing an important role in the motor control. In this paper, a novel method of fiction compensation using neuro-fuzzy architecture has been shown to significantly improve the performance of a DC motor system with nonlinear friction characteristics. The structure of the controller is the neuro-fuzzy network with the TS(Takagi-Sugeno) model. A back-propagation neural network based on a gradient descent algorithm is employed, and all of its parameters can be on-line trained. The performance of the proposed controller is compared with both a conventional neuro-controller and a PI controller.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.05a
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• pp.415-417
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• 2005

In this paper, we propose a intelligent controller design method for the water level control of the power plant drum in the form of nonminimum phase system. The proposed method is based on T. Takagi and M. Sugeno's fuzzy model. And we illustrate the improved characteristics as the simulation results, comparing with the conventional the PID and LQ controller design method

• Journal of the Korean Society of Industry Convergence
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• v.10 no.4
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• pp.271-274
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• 2007

In this paper, we propose a intelligent controller design method for the water level control of the power plant drum in the form of nonminimum phase system. The proposed method is based on T. Takagi and M. Sugeno's fuzzy model. And we illustrate the improved characteristics as the simulation results, comparing with the conventional the PID and LQ controller design method.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.146-154
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• 2006

In this paper, we propose direct and indirect adaptive fuzzy sliding mode control approaches for a class of nonaffine nonlinear systems. In the direct case, we use the implicit function theory to prove the existence of an ideal implicit feedback linearization controller, and hence approximate it to attain the desired performances. In the indirect case, we exploit the linear structure of a Takagi-Sugeno fuzzy system with constant conclusion to establish an affine-in-control model, and therefore design an indirect adaptive fuzzy controller. In both cases, the adaptation laws of the adjustable parameters are deduced from the stability analysis, in the sense of Lyapunov, to get a more accurate approximation level. In addition to their robustness, the design of the proposed approaches does not require the upper bounds of both external disturbances and approximation errors. To show the efficiency of the proposed controllers, a simulation example is presented.