• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.32-42
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• 2001

In this paper, a parameter estimator is developed for the plant model whose structure is represented by the Takagi-Sugeno model. The essential idea behind the on-line estimation is the comparison of the measured stated with the state of an estimation model whose structure is the same as that of the parameterized model. Based on the parameter estimation scheme, and indirect Model Reference Adaptive Fuzzy control(MRAFC) scheme is proposed to provide asymptotic tracking of a reference signal for the systems with uncertain for slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop systems. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.508-512
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• 2016

This paper discusses a Takagi-Sugeno (T-S) fuzzy controller design problem for a phugoid model-based multi-agent system. The error between the state of a phugoid model and a reference is defined to construct a multi-agent system model. A T-S fuzzy model of the multi-agent system is built by introducing a nonlinear controller. A fuzzy controller is then designed to stabilize the T-S fuzzy model, where the synthesis condition is represented in terms of linear matrix inequalities.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.526-530
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• 2011

This paper presents a new fuzzy model-based design approach for waypoints-tracking control of nonlinear underwater vehicles (UUVs) on a horizontal plane. The waypoints-tracking control problem is converted into the stabilization one for the error model between the given nonlinear UUV and the waypoints. By using the sector nonlinearity, the error model is modeled in Takagi-Sugeno's form. We then derive stabilization conditions for the error model in the format of linear matrix inequality. A numerical simulation is provided to illustrate the effectiveness of the proposed methodology.

• 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.

• 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.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.17-20
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• 2002

This paper proposes a new 75 fuzzy model approximation method which reduces error in nonlinear fuzzy model approximation over the V-type decision rules. Employing rational Bezier curves used in computer graphics to represent curves or surfaces, the proposed method approximates the decision rule by constructing a tractable linear equation in the highly non-linear fuzzy rule interval. This algorithm is applied to the self-adjusting air cushion for spinal cord injury patients to automatically distribute the patient's weight evenly and balanced to prevent decubitus. The simulation results indicate that the performance of the proposed method is bettor than that of the conventional TS Fuzzy model in terms of error and stability.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.58-65
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• 2003

In this paper, a new model, which is a Takagi-Sugeno fuzzy model, for mobile robot is presented. A controller, consisting of two loops the one of which is the inner state feedback loop designed for stability and the outer loop is a PI controller designed for tracking the reference input, is suggested. Because the robot dynamics is nonlinear, it requires the controller to be insensitive to the nonlinear term. To achieve this objective, the model is developed by well known T-S fuzzy model. The design algorithm of inner state-feedback loop is regional pole-placement. In this paper, regions, for which poles of the inner state feedback loop are lie in, are formulated by LMI's. By solving these LMI's, we can obtain the state feedback gains for T-S fuzzy system. And this paper shows that the PI controller is equivalent to the state feedback and the cost function for reference tracking is equivalent to the LQ(linear quadratic) cost. By using these properties, it is also shown in this paper that the PI controller can be obtained by solving the LQ problem.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.95-105
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• 2001

In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control(AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno(T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model(SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied for the control of an uncertain Lorenz system based on T-S fuzzy model such as stabilization, synchronization and chaotic model following control(CMFC).

• 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 ...

• Journal of Electrical Engineering and information Science
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• v.2 no.4
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• pp.28-36
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• 1997

In this paper, robust stability analysis for the fuzzy feedback linearization regulator is presented. Well-known Takagi-Sugeno fuzzy model is used as the MISO nonlinear plant model. Uncertainty and disturbance are assumed to be included in the model structure with known bounds. For these structured uncertainty and disturbances, robust stability of the close system is analyzed in both input-output sense and Lyapunov sense. The robust stability conditions are proposed by using multivariable circle criterion and the relationship between input-output stability and Lyapunov stability. The proposed stability analysis is illustrated by a simple example.