• Journal of Drive and Control
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• v.17 no.1
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• pp.44-50
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• 2020

For the requirement of accurate tracking control and the safety of physical human-robot interaction, torque control is basically desirable for humanoid robots. Because of the complexity of humanoid robot dynamics, the TDC (time-delay control) is practical because it does not require a dynamic model. However, there occurs a considerable error due to discontinuous non-linearities. To solve this problem, the TDC-FLC (fuzzy logic compensator) is applied to humanoid robots. The applied controller contains three factors: a TDE (time-delay estimation) factor, a desired error dynamic factor, and FLC to suppress the TDE error. The TDC-FLC is easy to execute because it does not require complicated humanoid dynamic calculations and the heuristic fuzzy control rules are intuitive. TDC-FLC is implemented on the whole body of a humanoid, not on biped legs even though it is performed by a virtual humanoid robot. The simulation results show the validity of the TDC-FLC for humanoid robots.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.5
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• pp.407-414
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• 2001

In this paper, we present a decentralized robust adaptive fuzzy controller for the transient stability and voltage regulation of a multimachine power system under a sudden fault. Power systems have uncertain dynamics due to various effects such a lightning, severe storms and equipment failure in addition to interconnections between generators. Hence a robust controller to deal with these uncertainties in needed. The fuzzy systems are adapted using a Lyapunov-based design and the stability of each closed-loop system is guaranteed. Simulation results show that satisfactory performance is achieved by proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1723-1728
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• 2005

This article describes the fuzzy control of the 3-dimensional motion of the container cranes used in dockside container terminals. The container is suspended by four flexible cables via spreader, and due to the disturbances such as the wind and acceleration of cranes, the container undergoes translational(sway) and rotational position errors. And due to the uncertainty of weight and rotational inertia, accurate position control of container crane is difficult to realize. This paper, based on the analysis of 3-dimensional dynamics of container moving systems, describes the design of the fuzzy controller, which does not require the computation time to optimize the distribution of cable tension. The developed controller is shown effective in controlling the container position in the presence of gust and parameter uncertainties.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.792-797
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• 2009

This paper presents the evaluation of PID and fuzzy control logic for the lateral position control of a moving web in roll-to-roll (R2R) printed electronics. In addition, we report the implementation of computer simulation software that enables us to develop the control logic in a graphic user interface and to test the controller performance in 3D dynamic environment. A mathematical model of the web dynamics is described first to explain the lateral motion of a moving web. Based on the model, PID and fuzzy controllers are designed, and embedded in the simulation software. Under the simulation conditions for fabricating RFID antenna by R2R printing, the results indicate that the fuzzy controller shows a better performance and can be more suitable for R2R multi-layer printed electronics.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.161-163
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• 2005

This paper is concerned with an Adaptive Fuzzy Sliding Mode Control(AFSMC) that the fuzzy systems are used to approximate the unknown functions of nonlinear system. In the adaptive fuzzy system. we adopt the adaptive law to approximate the dynamics of the nonlinear plant and to adjust the parameters of AFSMC. The stability of the suggested control system is proved via Lyapunov stability theorem. and convergence and robustness properties are demonstrated. The simulation results demonstrate that the performance is improved and the system also exhibits stability.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.708-713
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• 2000

A research on the AFC(Auto Feedrate Control) by a fuzzy controller using a tool dynamometer and motor currents was conducted. For simulations, cutting dynamics of end-milling process was modeled by geometric relationship between tool and work-piece. The fuzzy logic controller was employed to track the desired cutting force and showed good performance in simulations and several experiments. The spindle motor currents was modeled to estimate cutting force and successfully used for the AFC.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.6
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• pp.55-61
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• 2003

Control of multi-link robot arms is a challenging and difficult problem because of the highly nonlinear dynamics. Independent joint adaptive scheme is developed for control of robot manipulators based on Sugeno-type of fuzzy logic. Fuzzy logic system is used to approximate the coupling forces among the joints, coriolis force, centrifugal force, gravitational force, and frictional forces. The proposed scheme does not require an accurate manipulator dynamic, and it is proved that closed-loop system is asymptotic stable despite the gross robot parameter variations. Numerical simulations for three-axis PUMA robot are included to show the effectiveness of controller.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.83-85
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• 1997

In this paper, a self-tunning fuzzy inference technique for stabilization of the inverted pendulum system is proposed. The facility of this self-tunning fuzzy controller which has swing-up control mode and a stabilization one, moves a pendulum in an initial natural stable equilibrium point and a cart in arbitrary position, to an unstable equilibrium point and a center of rail. Specially, the virtual equilibrium point(${\phi}_{VEq}$) which describes functionally considers the interactive dynamics between a position of cart and a angle of inverted pendulum is introduced. And comparing with the convention optimal controller, the proposed self-tunning fuzzy inference structure made substantially the inverted pendulum system robust and stable.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.236-236
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• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2037-2045
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• 2016

This paper presents a fuzzy-model-based design approach to the buoyancy and moment controls of a class of nonlinear underwater glider. Through the linearization and the sector nonlinearity methodologies, the underwater glider dynamics is represented by a Takagi-Sugeno (T-S) fuzzy model. Sufficient conditions are derived to guarantee the asymptotic stability of the closed-loop system in the format of linear matrix inequality (LMI). Simulation results demonstrate the effectiveness of the proposed buoyancy and moment controllers for the underwater glider.