• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.293-299
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• 2003

This paper discusses a design of intelligent controller with time delay for Internet-based remote control. The finite Markovian process is adopted to model the input delay of the overall control system. It is assumed that the zero and hold devices are used for control input. The Takagi-Sugeno (T-S) fuzzy system with uncertain input delay is utilized to represent nonlinear plant. The continuous-time T-S fuzzy system with the Markovian input delay is discretized for easy handling delay, accordingly, the discretized T-S fuzzy system is represented by a discrete-time T-S fuzzy system with jumping parameters. The robust stochastic stabilizibility of the jump T-S fuzzy system is derived and formulated in terms of linear matrix inequalities (LMIs). An experimental results is provided to visualize the feasibility of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.520-525
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• 2003

This paper concerns a design methodology of observer-based output-feedback digital controller for Takagi-Sugeno(TS) fuzzy systems using intelligent digital redesign (IDR). The term of IDR involves converting an analog fuzzy-mode-based controller into an equivalent digital one in the sense of state-matching. The considered IDR problem is viewed as convex minimization problems of the norm distances between linear operators to be matched. The stability condition is easily embedded and the separations principle is explicitly shown.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.302-306
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• 2011

This paper presents Takagi-Sugeno (T-S) fuzzy-model-based controller for depth control of autonomous underwater vehicles(AUVs). Through sector nonlinearity methodology, The nonlinear AUV is represented by T-S fuzzy model. By using the Lyapunov function, the design condition of controller is derived to guarantee the performance of depth control in the format of linear matrix inequality (LMI). An example is provided to illustrate the effectiveness of the proposed methodology.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.335-340
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• 2008

In this paper, a decentralized fuzzy output feedback controller for nonlinear interconnected systems with time delay is proposed. The nonlinear interconnected system is represented to fuzzy system using Takagi-Sugeno (T-S) fuzzy model. The decentralized output feedback controller is designed(or stability of subsystems of the fuzzy interconnected system. The stable condition of the closed-loop subsystem is represented to the linear matrix inequality (LMI) form and control gain is obtained by LMI. An example is given to show the verification discussed throughout the paper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.523-527
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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 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.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.44-49
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• 1999

This paper presents a new T-S(Tae-Sugeno) fuzzy controller design method satisfying the output energy bound. Maximum output energy via a quadratic Lyapunov function to obtain the bound on output energy is derived. LMI(Linear Matrix Inequality) problems which satisfy an output energy bound for both of the continuous-time and discrete-time T-S fuzzy control system are also derived. Solving these LMIs simultaneously, we find a common symmetric positive definite matrix P which guarantees the global asymptotic stability of the system and stable feedback gains K's satisfying the output energy bound. A simple example demonstrates validity of the proposed design method.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.225-235
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• 2005

This paper presents the robust stability analysis and design methodology of the fuzzy feedback linearization control systems. Uncertainty and disturbances with known bounds are assumed to be included in the Takagi-Sugeno (TS) fuzzy models representing the nonlinear plants. $L_2$ robust stability of the closed system is analyzed by casting the systems into the diagonal norm bounded linear differential inclusions (DNLDI) formulation. Based on the linear matrix inequality (LMI) optimization programming, a numerical method for finding the maximum stable ranges of the fuzzy feedback linearization control gains is also proposed. To verify the effectiveness of the proposed scheme, the robust stability analysis and control design examples are given.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.658-662
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• 2004

This paper develops a stability analysis and controller synthesis methodology for a continuous-time affine Takagi-Sugeno (T-S) fuzzy systems with parametric uncertainties. Affine T-S fuzzy system can be an advantage because it may be able to approximate nonlinear functions to high accuracy with fewer rules than the homogeneous T-S fuzzy systems with linear consequents only. The analysis is based on Lyapunov functions that are continuous and piecewise quadratic. The search for a piecewise quadratic Lyapunov function can be represented in terms of bilinear matrix inequalities (BMIs). A simulation example is given to illustrate the application of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.886-889
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• 2017

An active Fault Tolerant Model Predictive Control (FTMPC) using Fuzzy scheduler is developed. Fault tolerant Control (FTC) system stages are broadly classified into two namely Fault Detection and Isolation (FDI) and fault accommodation. Basically, the faults are identified by means of state estimation techniques. Then using the decision based approach it is isolated. This is usually performed using soft computing techniques. Fuzzy Decision Making (FDM) system classifies the faults. After identification and classification of the faults, the model is selected by using the information obtained from FDI. Then this model is fed into FTC in the form of MPC scheme by Takagi-Sugeno Fuzzy scheduler. The Fault tolerance is performed by switching the appropriate model for each identified faults. Thus by incorporating the fuzzy scheduled based FTC it becomes more efficient. The system will be thereafter able to detect the faults, isolate it and also able to accommodate the faults in the sensors and actuators of the Continuous Stirred Tank Reactor (CSTR) process while the conventional MPC does not have the ability to perform it.