• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.137-140
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• 2001

In this paper, we design 75(Takagi-Sugeno) fuzzy controllers for the systems that can be represented by the 75 fuzzy model. We use inverse optimal approach in which the cost function is determined later than the Lyapunov function and its corresponding control input satisfying the design requirements such as stability and decay rate. The obtained design procedure is in the form of solving LMI(Linear Matrix Inequalities), thus very efficient in practice.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.270-275
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• 2005

This paper presents a fuzzy model-based adaptive approach for synchronization of chaotic systems which consist of the drive and response systems. Takagi-Sugeno (T-S) fuzzy model is employed to represent the chaotic drive and response systems. Since the parameters of the drive system are assumed unknown, we design the response system that estimates the parameters of the drive system by adaptive strategy. The adaptive law is derived to estimate the unknown parameters and its stability is guaranteed by Lyapunov stability theory. In addition, the controller in the response system contains two parts: one part that can stabilize the synchronization error dynamics and the other part that estimates the unknown parameters. Numerical examples, including Doffing oscillator and Lorenz attractor, are given to demonstrate the validity of the proposed adaptive synchronization approach.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.3
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• pp.157-162
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• 2005

This paper presents an adaptive compensator using the fuzzy systems for robot manipulator with unknown frictions. In general, frictions are neglected or dynamic frictions are only considered in robot control theories. The proposed control method considers viscous frictions as well as dynamic frictions. Using the property that the frictions of joints are decoupled, SISO-fuzzy systems are utilized to approximate each friction. The stability of overall control system is proven and the adaptive laws are derived based on Lyapunov stability theorey. To verify the validity of the proposed control strategy, the results of computer simulations are shown for 2-link robot manipulator. The ability of approximating of the fuzzy system is also shown.

• Journal of Astronomy and Space Sciences
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• v.29 no.4
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• pp.389-395
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• 2012

The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS). An ANFIS produces a control signal for one of the three axes of a spacecraft's body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw) and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.549-554
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• 2011

This paper presents the stabilization method for polynomial fuzzy large-scale system by using output feedback controller. Each sub system of the large-scale system is transformed into polynomial fuzzy model, and then output feedback controller is designed to stabilize the large-scale system. Stabilization condition is derived as sum-of-square (SOS) condition by applying the polynomial Lyapunov function. This condition can be easily solved by SOSTOOLS which is the third party of the MATLAB. From these solutions, output feedback controller gain can be obtained by SOS condition. Finally, a simulation example is presented to illustrate the effectiveness and the suitability of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.28-35
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• 2012

This paper presents the method for $H_{\infty}$ fuzzy controller design of discrete-time fuzzy Markovian jump systems with state and input time delays. The Takagi and Sugeno fuzzy model is employed to represent a delayed nonlinear system that possesses Markovian jump parameters. A stochastic mode dependent Lyapunov function is employed to analyze the stability and $H_{\infty}$ disturbance attenuation performance of the fuzzy Markovian jump systems with state and input time delays. A sufficient condition for the existence of fuzzy $H_{\infty}$ controller is given in terms of matrix inequalities. Also numerical example is presented to illustrate the efficiency of the proposed design method.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2308-2310
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• 2001

In the adaptive fuzzy sliding mode control, from a set of a fuzzy IF-THEN rules adaptive fuzzy sliding mode control whose parameters are adjusted on-line according to some adaptation laws is constructed for the purpose of controlling the plant to track a desired trajectory. Most of the research works in nonlinear controller design using fuzzy systems consider the affine system with fixed grid-rule structure based on system state availability. The fixed grid-rule structure makes the order of the controller big unnecessarily, hence the on-line fuzzy rule structure and fuzzy observer based adaptive fuzzy sliding mode controller is proposed to solve system state availability problems. Therefore, adaptive laws of fuzzy parameters for state observer and fuzzy rule structure are established implying whole system stability in the sense of Lyapunov.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.6
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• pp.828-839
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• 1995

Although the fuzzy logic controller(FLC) has been adopted in many engineering applications, its performance is not guaranteed since there is no definite theoretic analysis. It may be the main factor that one hesitates to adopt the FLC in critical applications. In this paper, observing the similarity in the pattern of control input between the FLC and a conventional robust controller, i.e., the variable structure controller, we present theoretic analysis for robustness of a fuzzy control system based on the Lyapunov theory.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.76-79
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• 2001

This paper is concerned with a stability analysis of Madam Type fuzzy systems. It Introduces the canonical form of an unforced fuzzy system and its stability theorem suggested in the previous study. Then it gives new simplified stability conditions based on the Lyapunov function method. A common positive definite matrix in the stability conditions is searched by the LMI method.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.921-923
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• 1999

In adaptive fuzzy control, fuzzy systems are used to approximate the unknown plant nonlinearities. However, because of the approximating error introduced into the feedback loop, it is difficult to guarantee the stability of the adaptive control algorithm. This paper presents a robust control algorithm against the reconstruction error and uniform boundedness of the all signals is estabilished in the Lyapunov sense.