• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2143-2145
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• 2002

This paper proposes the control problem of an inverted pendulum system based on adaptive fuzzy sliding mode. The universal approximating capability, learning ability, adaptation capability and disturbance rejection are collected in one control strategy. The proposed scheme does not require an accurate dynamic model and the joint acceleration measurement, yet it guarantees asymptotic trajectory tracking. Experimental results perform with an inverted pendulum to show the effectiveness of the approach.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.512-514
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• 1998

The paper presents a model reference adaptive control containing a fuzzy algorithm for tuning the gain coefficient which adjusts the level of the fuzzy controller output. The synthesis of a fuzzy tuning algorithm has been performed for the inverted pendulum system. The computer simulation results have proved the efficiency of the proposed method, showing stable system responses.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.39-42
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• 1998

This paper proposes an adaptive fuzzy control scheme for a class of continuous-time nonlinear dynamic systems for which an explicit linear parameterization of the uncertainty is either unknown or impossible. In order to improve robustness under approximation errors and disturbances the proposed scheme includes deadzone in adaptation laws which varies its size adaptively. The assumption of known bounds on the approximation errors and disturbances is not required since those are estimated using adaptation laws. The overall adaptive scheme is proven to guarantee uniform ultimate boundedness in the Lyapunov sense.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.4
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• pp.37-43
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• 1993

As an approach to develope the intelligent control scheme, this paper will propose an adaptive neural-fuzzy control scheme. The proposed neural-fuzzy control system, which consists of the Fuzzy-Neural Controller(FNC) and Model Neural Network(MNN), has two important characteristics of adaptation and learning. The error back propagation algorithm has been adopted as a learning technique.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.47-53
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• 2010

The paper presents a method for decision the optimal cycle time during the vehicle traffic control in single intersection with AHP. To solve this uncertainty optimization problem, the optimization index in the form of linear addition and fuzzy measurement is assumed and fuzzy integral is used. Examples of solution for two cases of optimal cycle time in two traffic controller are presented and compared.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.134-138
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• 1996

Refuse incineration plant operations involve many kinds of uncertain factors, such as the variable physical properties of refuse as fuel and the complexity of the burning phenomenon. That makes it very difficult apply conventional control methods to the combustion control of the refuse. In this paper, an adaptive fuzzy model predictive controller is proposed for the combustion control of the refuse. In this paper, an adaptive fuzzy model predictive controller is proposed for the combustion control of the refuse. And computer simulation was carried out to evaluate performance of the proposed controller.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.995-1002
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• 2000

In this paper, fuzzy logic controllers(FLC) are designed for control of flight. For tuning FLC, we used adaptive evolutionary algorithms(AEA) which uses a genetic algorithm(GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations. We used AEA to search for optimal settings of the membership functions shape and gains of the inputs and outputs of FLC. Finally, the proposed controller is applied to the high-angle-of-attack flight system for a supermaneuverable version of the f-18 aircraft and compares with other methods.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.6
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• pp.555-561
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• 2012

This paper describes the design of a fuzzy adaptive sliding mode controller for tracking control of robotic manipulators. The proposed controller incorporates a modified traditional sliding mode controller to drive the system state to a sliding surface and then keep the system state on this surface, and a fuzzy logic controller to accelerate the reaching phase. The stability of the control system is ensured by using Lyapunov theory. To verify the effectiveness of the proposed controller, computer simulation is conducted for a five-bar planar robotic manipulator. The simulation results show that the proposed controller can improve the reaching time and eliminate chattering of the control system at the same time.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.1-6
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• 2012

In this paper, an adaptive fuzzy control system is proposed for the speed control of the ASV (Autonomous Surface Vehicle) with nonaffine nonlinear system dynamics. We consider the turning speed of the screw propeller to be the control input instead of thrust so that we do not have to know the exact function between turning speed and thrust. But in this case, the ASV becomes a nonaffine nonlinear system because thrust is a nonlinear function of the turning speed. To solve this problem, we propose a Takagi-Sugeno fuzzy-model-based control system and simulation studies are performed. Simulation results show the effectiveness of the proposed control scheme.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.323-329
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• 2001

In this paper, we present a collaborative method for material delivery using a distributed vehicle agents system. Generally used AGV(Autonomous Guided Vehicle) systems in FA require extraordinary facilities like guidepaths and landmarks and have numerous limitations for application in different environments. Moreover in the case of controlling multi vehicles, the necessity for developing corporation abilities like loading and unloading materials between vehicles including different types is increasing nowadays for automation of material flow. Thus to compensate and improve the functions of AGV, it is important to endow vehicles with the intelligence to recognize environments and goods and to determine the goal point to approach. In this study we propose an interaction method between hetero-type vehicles and adaptive fuzzy logic controllers for sensor-based path planning methods and material identifying methods which recognizes color. For the purpose of carrying materials to the goal, simple color sensor is used instead vision system to search for material and recognize its color in order to determine the goal point to transfer it to. The proposed method reaveals a great deal of improvement on its performance.