• Journal of Advanced Marine Engineering and Technology
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• v.27 no.7
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• pp.852-861
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• 2003

In this paper, in order to improve the disadvantages of the fixed design-parameter fuzzy PID controller. a new fuzzy PID controller named a variable design-parameter fuzzy PID controller is suggested. The main characteristic of the suggested controller is to adjust design-parameters of the controller by comparing magnitudes between fuzzy controller inputs at each sampling time when controller inputs are measured. As a result. all fuzzy input partitioned spaces converge within a time-varying normalization scale. and the resultant PID control action can always be applied precisely regardless of operating input magnitudes. In order to verify the effectiveness of the suggested controller. several a computer simulations for a nonlinear system are executed and the control parameters of the variable design-parameter fuzzy PID controller are throughly analyzed.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.2
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• pp.228-236
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• 2004

In this paper. we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feed forward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand. feed forward neural networks provide salient features. such as learning and parallelism. In the proposed neuro-fuzzy controller. the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error back propagation algorithm as a learning rule. while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally. the effectiveness of the proposed controller is verified through computer simulation for an inverted pole system.

• Proceedings of the KIEE Conference
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• 2002.04a
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• pp.79-81
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• 2002

This paper develops a adaptive fuzzy controller based fuzzy logic control for high performance speed controller in permanent magnet synchronous motor(PMSM) drives. In the proposed system, fuzzy control is used to implement the direct controller as well as the adaptation mechanism. The operation of the direct fuzzy controller and the fuzzy logic based adaptation mechanism is studied. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed adaptive fuzzy controller is confirmed by performance results for PMSM drive system.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.3
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• pp.8-13
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• 2007

A hybrid fuzzy PID controller for a pneumatic chamber is proposed in this paper. First, a mathematical model of a pneumatic pressure servocontrol system was developed where separate implementations of a PID controller and a fuzzy controller were made. The experimental results using a step input signal revealed that the PID controller accurately controlled the steady-state pressure but did not robustly handle parameter variations in the system while the fuzzy controller provided a fast rise time and low overshoot of the pressure in the system. In order to attain the advantages of both the fuzzy and PID controllers, a hybrid control scheme was developed. The experimental results show that the hybrid fuzzy PID controller proposed in this study does indeed possess the advantages of both PID and fuzzy controllers. Hence, it can be concluded that the hybrid fuzzy PID controller is suited for high-precision control of pressure in a pneumatic chamber.

• Structural Engineering and Mechanics
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• v.43 no.2
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• pp.163-177
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• 2012

This paper presents a LMI(linear matrix inequality)-based fuzzy approach of modeling and active vibration control of geometrically nonlinear flexible plates with piezoelectric materials as actuators and sensors. The large-amplitude vibration characteristics and dynamic partial differential equation of a piezoelectric flexible rectangular thin plate structure are obtained by using generalized Fourier series and numerical integral. Takagi-Sugeno (T-S) fuzzy model is employed to approximate the nonlinear structural system, which combines the fuzzy inference rule with the local linear state space model. A robust fuzzy dynamic output feedback control law based on the T-S fuzzy model is designed by the parallel distributed compensation (PDC) technique, and stability analysis and disturbance rejection problems are guaranteed by LMI method. The simulation result shows that the fuzzy dynamic output feedback controller based on a two-rule T-S fuzzy model performs well, and the vibration of plate structure with geometrical nonlinearity is suppressed, which is less complex in computation and can be practically implemented.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.60-64
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• 1997

Designing fuzzy controller, there are difficulties that we have to determine fuzzy rules and shapes of membership functions which are usually obtained by the amount of trial-and-error or experiences from the experts. In this paper, to overcome these defects, genetic algorithms which is probabilistic search method based on genetics and evolution theory are used to determine fuzzy rules and fuzzy membership functions. We design a series compensation fuzzy controller, then determine basic structures, input-output variables, fuzzy inference methods and defuzzification methods for fuzzy controllers. We develop genetic algorithms which may search more accurate optimal solutions. For evaluating the fuzzy controller performances through experiments upon an actual system, we design the fuzzy controllers for the speed control of a DC series motor with nonlinear characteristics and show good output responses to reference inputs.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1168-1172
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• 2004

This paper presents a speed controller based on an adaptive fuzzy algorithm for high performance permanent magnet synchronous motor (PMSM) drives under parameter and load variations. In many speed tracking control systems PI controller has been used due to its simple structure and easy of design. PI controller, however, suffers from the electrical machine parameter variations and disturbances. In order to improve the tracking control performance under load variations, the PI controller parameters are modified during operation by adaptive fuzzy method. This method based on optimal fuzzy logic system has simple structure and computational simplicity. It needs only sample data which is obtained by optimal controller off-line. As the sample data implemented in the adaptive fuzzy system can be modified or extended, a flexible control system can be obtained. Simulation results show the usefulness of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1492-1495
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• 2005

This paper presents a new linear-matrix-inequality-based intelligent digital redesign (LMI-based IDR) technique to match the states of the analog and the digital T-S fuzzy control systems at the intersampling instants as well as the sampling ones. The main features of the proposed technique are: 1) the affine control scheme is employed to increase the degree of freedom; 2) the fuzzy-model-based periodic control is employed; and the control input is changed n times during one sampling period; 3) The proposed IDR technique is based on the approximately discretized version of the T-S fuzzy system; but its discretization error vanishes as n approaches the infinity. 4) some sufficient conditions involved in the state matching and the stability of the closed-loop discrete-time system can be formulated in the LMIs format.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.35-38
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• 1998

Abstracts In this paper, we propose a robust adaptive fuzzy control scheme using a sliding control input for tracking of a class of MISO nonlinear systems with unknown bounded external disturbances. In the proposed scheme, the nonlinearity is estimated adaptively via a fuzzy inference based on a fuzzy model. A sliding control input is introduced such that boundedness of all signals in the system is guaranteed even though the existence of a fuzzy approximation error and external disturbances. The controller parameters are updated by using a proposed adaptation law, which is similar 1-modification method. Computer simulation shows the effectiveness of the proposed control scheme.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.450-453
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• 1997

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. The result of simulations shows that the crane is just controlled for a very large swing angle of 1 radian within nearly one cycle.