• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.187-192
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• 2012

This paper is concerned with stabilization problem of continuous-time Takagi-Sugeno fuzzy systems. To do this, the stabilization problem is investigated based on the new fuzzy Lyapunov functions (NFLFs). The NFLFs depend on not only the fuzzy weighting functions but also their first-time derivatives. The stabilization conditions are derived in terms of linear matrix inequalities (LMIs) which can be solved easily by the Matlab LMI Toolbox. Simulation examples are given to illustrate the effectiveness of this method.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.46-51
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• 2004

his paper presents a novel design technique for the TS fuzzy classifier via linear matrix inequalities(LMI). To design the TS fuzzy classifier built by the TS fuzzy model, the consequent parameters are determined to maximize the classifier's performance. Differ from the conventional fuzzy classifier design techniques, convex optimization technique is used to resolve the determination problem. Consequent parameter identification problems are first reformulated to the convex optimization problem. The convex optimization problem is then efficiently solved by converting linear matrix inequality problems. The TS fuzzy classifier has the optimal consequent parameter via the proposed design procedure in sense of the minimum classification error. Simulations are given to evaluate the proposed fuzzy classifier; Iris data classification and Wisconsin Breast Cancer Database data classification. Finally, simulation results show the utility of the integrated linear matrix inequalities approach to design of the TS fuzzy classifier.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1162-1167
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• 2004

This paper presents the design methodology of digital fuzzy controller(DFC) for the systems with time-delay. We propose the fuzzy feedback controller whose output is delayed with unit sampling period and predicted. The analysis and the design problem considering time-delay become easy because the proposed controller is syncronized with the sampling time. The stabilization problem of the digital fuzzy system with time-delay is solved by linear matrix inequality(LMI) theory. Convex optimization techniques are utilized to solve the stable feedback gains and a common Lyapunov function for designed fuzzy control system. To show the effectiveness the proposed control scheme, the network control example is presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.153-156
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• 2001

This paper discusses a stochastic stabilization of Takagi-Sugeno (75) fuzzy system with Markovian input delay. 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 continuous-time 75 fuzzy system with the Markovian input delay is discretized for easy handling delay, accordingly, the discretized 75 fuzzy system is represented by a discrete-time 75 fuzzy system with jumping parameters. The stochastic stabilizibility of the jump 75 fuzzy system is derived and formulated in terms of linear matrix inequalities (LMls).

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.9
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• pp.851-856
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• 2011

In this paper, the fuzzy output-feedback controller is addressed for a discrete-time nonlinear interconnected systems with common input. The nonlinear interconnected system is represented by a T-S (Takagi-Sugeno) fuzzy model. Based on T-S fuzzy interconnected system, the fuzzy output-feedback controller is designed with common input. The stability condition of the closed-loop system is represented to the LMI (Linear Matrix Inequality) form. PEMFC model is given to show the verification of the controller discussed throughout the paper.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.615-623
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• 2008

This paper proposes an $H_{\infty}$ controller design method for Takagi-Sugeno (T-S) fuzzy systems using a fuzzy basis-function-dependent Lyapunov function. Sufficient conditions for the guaranteed $H_{\infty}$ performance of the T-S fuzzy control system are given in terms of linear matrix inequalities (LMIs). These LMI conditions are further used for a convex optimization problem in which the $H_{\infty}-norm$ of the closed-loop system is to be minimized. To facilitate the basis-function-dependent Lyapunov function approach and thus improve the closed-loop system performance, additional decision variables are introduced in the optimization problem, which provide an additional degree-of-freedom and thus can enlarge the solution space of the problem. Numerical examples show the effectiveness of the proposed method.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.248-254
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• 2000

This paper presents and output feedback fuzzy H(sup)$\infty$ control problem for a class of nonlinear systems with time-varying delayed state. The Takagi-Sugeno fuzzy model is employed to represent a nonlinear systems with time-varying delayed state. Using a single quadratic Lyapunov function, the globally exponential stability and disturance attenuation of the closed-loop fuzzy control system are discussed. Sufficient conditions for the existence of fuzzy H(sup)$\infty$ controllers are given in terms of matrix inequalities. Constructive algorithm for design of fuzzy H(sup)$\infty$ controller is also developed. A simulation example is given to illustrate the performance of the proposed design method.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.808-813
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• 1999

A fuzzy learning method to control an unstable and multivariable system is presented in this paper, Because the multivariable system has generally a coupling effect between the inputs and outputs, it is difficult to find its modeling equation or parameters. If the system is unstable, initial condition rules are needed to make it stable because learning is nearly impossible. Therefore, this learning method uses the initial rules and introduces a cost function composed of the actual error and error-rate of each output without the modeling equation. To minimize the cost function, we experimentally got the Jacobian matrix in the operating point of the system. From the Jacobian matrix, we can find the direction of the convergence in the learning, and the optimal control rules are finally acquired when the fuzzy rules are updated by changing the portion of the errors and error rates.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.571-576
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• 2004

This paper presents a delay dependent fuzzy $H_{\infty}$ controller design method for delayed fuzzy dynamic systems. Using delay-dependent Lyapunov function, the global exponential stability and $H_{\infty}$ performance problem arc discussed. A sufficient conditions for the existence of fuzzy controller is presented in terms of linear matrix inequalities(LMIs). A simulation example is given to illustrate the design procedures and performances of the proposed methods.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.5 s.98
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• pp.48-57
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• 1999

Applications of thresholding technique are based on the assumption that object and background pixels in a digital image can be distinguished by their gray level values. For the segmentation of more complex images, it is necessary to resort to multiple threshold selection techniques. This paper describes a new method for multiple threshold selection of gray level images which are not clearly distinguishable from the background. The proposed method consists of three main stages. In the first stage, a probability distribution function for a gray level histogram of an image is derived. Cluster points are defined according to the probability distribution function. In the second stage, fuzzy partition matrix of the probability distribution function is generated through the fuzzy clustering process. Finally, elements of the fuzzy partition matrix are classified as clusters according to gray level values by using max-membership method. Boundary values of classified clusters are selected as multiple threshold. In order to verify the performance of the developed algorithm, automatic inspection process of ball grid array is presented.