• Transactions on Control, Automation and Systems Engineering
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• v.1 no.2
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• pp.87-93
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• 1999

This paper proposes a systematic designs methodology for the Takagi-Sugeno (TS) model based fuzzy control systems with guaranteed stability and pre-specified transient performance for the application to a nonlinear magnetic bearing system. More significantly, in the proposed methodology , the control design problems which considers both stability and desired transient performance are reduced to the standard LMI problems . Therefore, solving these LMI constraints directly (not trial and error) leads to a fuzzy state-feedback controller such that the resulting fuzzy control system meets above two objectives. Simulation and experimentation results show that the proposed LMI-based design methodology yields only the maximized stability boundary but also the desired transient responses.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.506-515
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• 2006

This paper studies an intelligent digital control for nonlinear systems with multirate sampling. It is worth noting that the multirate control design is addressed for a given nonlinear system represented by Takagi-Sugeno (T-S) fuzzy models. The main features of the proposed method are that i) it is provided that the sufficient conditions for stabilization of the discrete-time T-S fuzzy system in the sense of Lyapunov stability criterion, which is can be formulated in the linear matrix inequalities (LMIs); and ii) the stability properties of the trivial solution of the digital control system can be deduced from that of the solution of its discretized versions. An example is provided for showing the feasibility of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.185-190
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• 2005

A systematic output tracking control design technique for robust control of Takagi-Sugeno (T-S) fuzzy systems with norm bounded uncertainties is developed. The uncertain T-S fuzzy system is first represented as a set of uncertain local linear systems. The tracking problem is then converted into the stabilization problem for a set of uncertain local linear systems thereby leading to a more feasible controller design procedure. A sufficient condition for robust asymptotic output tracking is derived in terms of a set of linear matrix inequalities. A stability condition on the traversing time instances is also established. The output tracking control simulation for a flexible-joint robot-arm model is demonstrated, to convincingly show the effectiveness of the proposed system modeling and controller design.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.133-138
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• 2013

This paper addresses a state consensus controller design technique of Takagi-Sugeno fuzzy model-based multi-agent systems in a continuous-time domain. We express the interconnection topology among the agents through graph theory. The design condition is represented in terms of linear matrix inequalities. Numerical example is provided to demonstrate the effectiveness of the proposed method.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.52-57
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• 2006

In this paper, we have proposed a new adaptive fuzzy control algorithm based on Takagi-Sugeno fuzzy model. The regulation problem for the uncertain SISO nonlinear system is solved by the proposed algorithm. Using the advanced stability theory, the stability of the state, the control gain and the parameter approximation error is proved. Unlike the existing feedback linearization based methods, the proposed algorithm can guarantee the global stability in the presence of the singularity in the inverse dynamics of the plant. The performance of the proposed algorithm is demonstrated through the problem of balancing and swing-up of an inverted pendulum on a cart.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.554-558
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• 2002

In this paper, we propose a robust indirect adaptive fuzzy state feedback regulator based on Takagi-Sugeno fuzzy model. The proposed adaptive fuzzy regulator is less sensitive to singularity than the conventional one based on the feedback linearization method. Furthermore, the proposed control method is applicable to not only plants with a perfect model but also plants with an imperfect model, which causes uncertainties. We verify the global stability of the proposed method by using Lyapunov method. In order to support the achievement, the application of the proposed adaptive fuzzy regulator to the control of a nonlinear system under the external disturbance is presented and the performance was verified by some simulation result.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.6
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• pp.80-86
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• 2002

In this paper, we present a method of fuzzy controller design for the power plant superheater in the form of bilinear system. For the steam temperature control, the input variables are constructed by the area of difference between the profiles estimated from bilinear observer and reference profiles, and the time rate of change. We estimate the control rules by T. Takagi and M. Sugeno's fuzzy model. The feasibilities of the suggested method are illustrated via the computer simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.169.3-169
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• 2001

This paper introduces a new approach to analysis of error convergence for a class of iterative learning control systems. First, a nonlinear plant is represented using a Takagi-Sugeno(T-S) fuzzy model. Then each iterative learning controller is designed for each linear plant in the T-S fuzzy model. From the view point of two-dimensional(2-D) system theory, we transform the proposed learning systems to a 2-D error equation, which is also established in the form of T-S fuzzy model. We analysis the error convergence in the sense of induced 2 L -norm, where the effects of disturbances and initial conditions on 2-D error are considered. The iterative learning controller design problem to guarantee the error convergence can be reduced to linear matrix inequality problems. In comparison with others, our learning algorithm ...

• 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.