• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.203-208
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• 1998

This paper presents an adaptive fuzzy controller using an neural network and adaptation algorithm. Reference-model following neuro-fuzzy controller(RMFNFC) is invesgated in order to overcome the difficulty of rule selecting and defects of the membership function in the general fuzzy logic controller(FLC). RMFNFC is developed to tune various parameter of the fuzzy controller which is used for the discrete nonlinear system control. RMFNFC is trained with the identification information and control closed loop error. A closed loop error is used for design criteria of a fuzzy controller which characterizes and quantize the control performance required in the overall control system. A control system is trained up the controller with the variation of the system obtained from the identifier and closed loop error. Numerical examples are presented to control of the discrete nonlinear system. Simulation results show the effectiveness of the proposed controller.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2849-2852
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• 1999

This paper describes the fuzzy PD controller using simplified indirect inference method. First, the fuzzy PD controller is derived from the conventional continuous time linear PD controller. Then the fuzzification, control-rule base, and defuzzification using SIIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional PD controller. which has the same linear structure. but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability. particularly when the process to be controlled is nonlinear. As the SIIM is applied, the fuzzy Inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the Proposed method has the capability of the high speed inference and extending the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control Performance of the one Proposed by D. Misir et at. Final)y. we simulated the contour control of the X-Y tables with direct control strategies using the proposed fuzzy PD controller.

• Journal of the Korean Society of Safety
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• v.14 no.2
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• pp.52-61
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• 1999

This paper proposes the application of fuzzy control for high performance control of induction motor using electric vehicles. A fuzzy controller converts a set of liguistic rules based on expert knowledge into a automatic control strategy. Such controllers have often been found superior to conventional controllers especially when information being processed is inexact and uncertain. A system with fast torque response is very beneficial in applications where direct self control (DSC) is highly desirable. The response of DSC is slower during startup and during change in command torque. Fuzzy control is used for implementation of DSC to improve its slow response. Simulation implementation of the fuzzy logic controller was carried out to verify the behavior of the controller. The simulation results with fuzzy control are compared with those of the conventional DSC. The starting flux and torque response and the responses to the step changes in command torque with fuzzy implementation show a considerable improvement over the conventional control. The steady state responses in both the cases are the same.

• Journal of Mechanical Science and Technology
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• v.18 no.4
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• pp.535-549
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• 2004

This paper proposes a sliding mode controller with fuzzy adaptive perturbation compensator(FAPC) to get a good control performance and reduce the chatter, The proposed algorithm can reduce the chattering because the proposed fuzzy adaptive perturbation compensator compensates the perturbation terms. The compensator computes the control input for compensating unmodeled dynamic terms and disturbance by using the observer-based fuzzy adaptive network(FAN) The weighting parameters of the compensate. are updated by on-line adaptive scheme in order to minimize the estimation error and the estimation velocity error of each actuator. Therefore, the combination of sliding mode control and fuzzy adaptive network gives the robust and intelligent routine to get a good control performance. To evaluate the control performance of the proposed approach, tracking control is experimentally carried out for the hydraulic motion platform which consists of a 6-DOF parallel manipulator.

• 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).

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.3
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• pp.97-103
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• 1995

In this paper, for fuzzy control of linear servo system using the moving coil type linear DC motor, we propose a new fuzzy control method using parameter tuning for membership functions. A proposed fuzzy control method tunes parameters of membership function to have an appropriate control input signal for system when error exceeds predefined value and makes an inference using conventional fuzzy control rules when error reduces to a predefined value. To verify usefulness of a proposed fuzzy control method, making simulation and experiment, we compare with characteristics for conventional fuzzy control method.

• Journal of Drive and Control
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• v.15 no.1
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• pp.28-37
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• 2018

When the wind speed rises above the rated wind speed, the produced power of the wind turbines exceeds the rated power. Even more, the excessive power results in the undesirable mechanical load and fatigue. A solution to this problem is pitch control of the wind turbines. This paper presents a systematic design method of a collective pitch controller for the wind turbines using a discrete fuzzy Proportional-Integral (PI) controller. Unlike conventional PI controllers, the fuzzy PI controller has variable gains according to its input variables. Generally, tuning the parameters of fuzzy PI controller is complex due to the presence of too many parameters strongly coupled. In this paper, a systematic method for the fuzzy PI controller is presented. First, we show the fact that the fuzzy PI controller is a superset of the PI controller in the discrete-time domain and the initial parameters of the fuzzy PI controller is selected by using this relationship. Second, for simplicity of the design, we use only four rules to construct nonlinear fuzzy control surface. The tuning parameters of the proposed fuzzy PI controller are also obtained by the aforementioned relationship between the PI controller and the fuzzy PI controller. As a result, unlike the PI controller, the proposed fuzzy PI controller has variable gains which allow the pitch control system to operate in broader operating regions. The effectiveness of the proposed controller is verified with computer simulations using FAST, a NREL's primary computer-aided engineering tool for horizontal axis wind turbines.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.284-284
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• 2000

In adaptive fuzzy control systems. fuzzy systems are used to approximate the unknown plant nonlinearities. Until now. most of the papers in the field of controller design for nonlinear system using fuzzy systems considers the affine system with fixed grid-rule structure based on system state availability. This paper considers observer-based nonlinear controller and dynamic fuzzy rule structure. Adaptive laws for fuzzy parameters for state observer and fuzzy rule structure are established so that the whole system is stable in the sense of Lyapunov.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.4
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• pp.285-294
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• 2000

This paper shows that the proposed fuzzy-sliding mode control algorithm for a SCARA robot could reduce the chattering due to sliding mode control and is robust against a change of payload and parameter uncertainties. That is, the chattering can be reduced by changing control input for compensating disturbances into a control input by fuzzy rules within a pre-determined dead zone. The experimental results show that the chattering can be reduced more effectively by the fuzzy-sliding mode control algorithm than the sliding mode control with two dead zones. It is proved experimentally that the proposed control algorithm is robust to a change of payload. The proposed control algorithm is implemented to the SCARA robot using a DSP(board) for high speed calculations.

• International Journal of Safety
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• v.10 no.1
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• pp.22-31
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• 2011

Seismic response control method of the bridge structures with semi-active control device, i.e., magneto-rheological (MR) damper, is studied in this paper. Design of various kinds of clipped optimal controller and fuzzy controller are suggested as a semi-active control algorithm. For determining the control force of MR damper, clipped optimal control method adopts bi-state approach, but the fuzzy control method continuously quantifies input currents through fuzzy inference mechanism to finely modulate the damper force. To investigate the performances of the suggested control techniques, numerical simulations of a multi-span continuous bridge system subjected to various earthquakes are performed, and their performances are compared with each other. From the comparison of results, it is shown that the fuzzy control system can provide well-balanced control force between girder and pier in the view point of structural safety and stability and be quite effective in reducing both girder and pier displacements over the existing control method.