• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.68-74
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• 2008

In this paper, a novel neural fuzzy control method is proposed to control nonlinear systems. A standard PD-like fuzzy controller is designed and used as a main controller for the system. Then a neural network controller is added to the reference trajectories to form a neural-fuzzy control structure and used to compensate for nonlinear effects. Two neural-fuzzy control schemes based on two well-known neural network control schemes, the feedback error learning scheme and the reference compensation technique scheme as well as the standard PD-like fuzzy control are studied. Those schemes are tested to control the angle and the position of the inverted pendulum and their performances are compared.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2006.03a
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• pp.495-502
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• 2006

Seismic performance of semi-active fuzzy control algorithm to operate MR dampers for coupling adjacent building is investigated in this paper. In the proposed semi-active control technique, the fuzzy logic is used as a method to adjust input voltage to MR damper. In order to validate control performance of proposed technique, the seismic performance of the semi-active fuzzy control system is compared with that of passive control system where the input voltage to MR damper is set to display maximum damping force. The simulated results show that the semi-active fuzzy control technique effectively regulates the trade-off existing between seismic responses of two buildings subject to various earthquake excitations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.137-144
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• 1998

This paper proposed a optimal scale factors technique of a fuzzy-neural network for a load frequency control of two areas power system. The optimal scale factors control technique is optimize from an initial fuzzy logic control rule, and then is learned with an error back propagation learning algorithm of the fuzzy-neural network. In application two areas the load frequency control of the power system, it hopes to have response characteristic better than optimal control technique which is the conventional control technique and to show to minimize a frequency deviation and reaching and settling time of a tie line power flow deviation

• Journal of the Korean Society of Hazard Mitigation
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• v.4 no.1 s.12
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• pp.33-40
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• 2004

The objective of this study is to develop single dam operation models for flood control using Fuzzy control technique, which can improve flood controllability. We set control rules by water level and inflow, and developed three models Fuzzy I, II, III according to rule to decide outflow. Fuzzy I model consists of six rules considering only flood control and Fuzzy II model considers the effect of water use by increasing water level at the end of flood control period as well as flood control during the same period. Finally, Fuzzy m is an adaptive model designed to perform multipurpose dam operation for both flood control and water use simultaneously based on a control rules.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.227-232
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• 2006

In this paper, we propose a systematic method for intelligent digital redesign of a fuzzy-model-based controller for continuous-time nonlinear system which may also contain system uncertainties. The continuous-time uncertain TS fuzzy model is first contructed to represent the uncertain nonlinear system. A parallel distributed compensation(PDC) technique is then used to design a fuzzy-model-based controller for both stabilization. The designed continuous-time controller is then converted to an equivalent discrete-time controller by using a globally intelligent digital redesign method. This new technique is designed by a global matching of state variables between analog control system and digital control system. This new design technique provides a systematic and effective framework for integration of the fuzzy-model-based control theory and the advanced digital redesign technique for nonlinear systems with uncertainties. Finally, Chaotic Lorenz system is used as an illustrative example to show the effectiveness and the feasibility of the developed design method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.378-381
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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 fuzzy-model-based periodic control is employed, and the control input is changed n times during one sampling period; 2) 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. 3) 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.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.244-249
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• 1998

In this paper, the hybrid state space self-tuning control technique Is studied within the framework of fuzzy systems and dual-rate sampling control theory. We show that fuzzy modeling techniques can be used to formulate chaotic dynamical systems. Then, we develop the hybrid state space self-tuning fuzzy control techniques with dual-rate sampling for digital control of chaotic systems. An equivalent fast-rate discrete-time state-space model of the continuous-time system is constructed by using fuzzy inference systems. To obtain the continuous-time optimal state feedback gains, the constructed discrete-time fuzzy system is converted into a continuous-time system. The developed optimal continuous-time control law is then convened into an equivalent slow-rate digital control law using the proposed digital redesign method. The proposed technique enables us to systematically and effective]y carry out framework for modeling and control of chaotic systems. The proposed method has been successfully applied for controlling the chaotic trajectories of Chua's circuit.

• International Journal of Ocean System Engineering
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• v.3 no.2
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• pp.50-60
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• 2013

This paper presents a novel approach to the design of an adaptive fuzzy sliding mode controller for depth control of an autonomous underwater vehicle (AUV). So far, AUV's dynamics are highly nonlinear and the hydrodynamic coefficients of the vehicles are difficult to estimate, because of the variations of these coefficients with different operating conditions. These kinds of difficulties cause modeling inaccuracies of AUV's dynamics. Hence, we propose an adaptive fuzzy sliding mode control with novel fuzzy adaptation technique for regulating vertical positioning in presence of parametric uncertainty and disturbances. In this approach, two fuzzy approximator are employed in such a way that slope of the linear sliding surface is updated by first fuzzy approximator, to shape tracking error dynamics in the sliding regime, while second fuzzy approximator change the supports of the output fuzzy membership function in the defuzzification inference module of fuzzy sliding mode control (FSMC) algorithm. Simulation results shows that, the reaching time and tracking error in the approaching phase can be significantly reduced with chattering problem can also be eliminated. The effectiveness of proposed control strategy and its advantages are indicated in comparison with conventional sliding mode control FSMC technique.

• Structural Engineering and Mechanics
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• v.36 no.3
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• pp.381-399
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• 2010

A fuzzy hybrid control technique using a semi-active tuned mass damper (STMD) has been proposed in this study for mitigation of wind induced motion of a tall building. For numerical simulation, a third generation benchmark is employed for a wind-excited 76-story building. A magnetorheological (MR) damper is used to compose an STMD. The proposed control technique employs a hierarchical structure consisting of two lower-level semi-active controllers (sub-controllers) and a higher-level fuzzy hybrid controller. Skyhook and groundhook control algorithms are used as sub-controllers. When a wind load is applied to the benchmark building, each sub-controller provides different control commands for the STMD. These control commands are appropriately combined by the fuzzy hybrid controller during realtime control. Results from numerical simulations demonstrate that the proposed fuzzy hybrid control technique can effectively reduce the STMD motion as well as building responses compared to the conventional hybrid controller. In addition, it is shown that the control performance of the STMD is superior to that of the sample TMD and comparable to an active TMD, but with a significant reduction in power consumption.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.297-300
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• 1997

This paper uses genetic algorithm (GA) for optimal control. GA can find optimal control profile, but the profile may be oscillating feature. To make profile smooth, fuzzy genetic algorithm (FGA) is proposed. GA with fuzzy logic techniques for optimal control can make optimal control profile smooth. We describe the Fuzzy Genetic Algorithm that uses a fuzzy knowledge based system to control GA search. Result from the simulation example shows that GA can find optimal control profile and FGA makes a performance improvement over a simple GA.