• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.43-48
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• 2010

This article presents the experimental studies of controlling angle and position of the inverted pendulum system using neural network to compensate for errors caused due to fuzzy controller. Although fuzzy control method can deal with nonlinearities of the system, fixed fuzzy rules may not work and result in tracking errors in some cases. First, a nominal Takagi-Sugeno (TS) type fuzzy controller with fixed weights is used for controlling the inverted pendulum system. Then the neural network is added at the reference input to form the reference compensation technique (RCT)control structure. Neural network modifies the input trajectories to improve system performances by updating internal weights in on-line fashion. The back-propagation learning algorithm for neural network is derived and used to update weights. Control hardware of a DSP 6713 board to have real time control is implemented. Experimental results of controlling inverted pendulum system are conducted and performances are compared.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.403-405
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• 1997

In this paper, a navigation system based on fuzzy logic controllers is developed for a mobile robot in an unknown environment. The structure of this fuzzy navigation system features sensor system, fuzzy controllers for motion planning and the motion control system for real-time execution.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.608-611
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• 2000

We introduce Adaptive Fuzzy Impedance Controller for position and force control when robot contact with environment. Because Robot and environment was always effected by nonlinear conditions, it need to deal with parameter’s uncertainty. For solving this problem, it induced Fuzzy System in Impedance Control so fuzzy system is impedance’s stiffness gain. We apply adaptive fuzzy impedance controller in One-Link Robot System, it shows the good performance on desired position control and force control about contacting with arbitrarily environment.

• Journal of Biosystems Engineering
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• v.25 no.2
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• pp.107-114
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• 2000

Low temperature storage method is to increase the value of agricultural products by reducing quality loss and regulate consignment time by controlling respiration rates of agricultural products. Respiration rate of agricultural products depends on several factors such as temperature, moisture, gas composition and a microbe inside the storage room. Temperature is the most important factor among these, which affects respiration rate and causes low or high temperature damage. Fuzzy logic was used to control the temperature of a storage room ,which uses information of uncertain facts and mathematical model for room temperature control . Room temperature was controlled better by using fuzzy logic control method rather than on-off control method. Refrigerant flow rates and temperature deviations were measured for on-off system using TEV(temperature expansion valve) and for fuzzy system using EEV(Electrical Expansion Valve) . Temperature of the Storage room was lowered faster by using fuzzy system than on -off system. Temperature deviation was -0.6~+0.9$^{\circ}C$ for on-off system and $\pm$0.2$^{\circ}C$ for fuzzy system developed. Temperature deviation and variation of temperature deviation were used as inout parameters for fuzzy system. The most suitable input and output value were found by experiment. Cooling rate of the storage room decreased while temperature deviation increased for the sampling time of 20 sec.

• Smart Structures and Systems
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• v.21 no.4
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• pp.389-395
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• 2018

This paper presents the application of a semi-active fuzzy based control system for seismic response reduction of a single degree-of-freedom (SDOF) framed structure using a Magnetorheological (MR) damper. Semi-active vibration control with MR dampers has been shown to be a viable approach to protect building structures from earthquake excitation. Moreover, intelligent damping systems based on soft-computing techniques such as fuzzy logic models have the inherent robustness to deal with typical uncertainties and non-linearities present in civil engineering structures. Thus, the proposed semi-active control system uses fuzzy logic based models to simulate the behavior of MR damper and also to develop the control algorithm that computes the required control signal to command the actuator. The results of the numerical simulations show the effectiveness of the suggested semi-active control system in reducing the response of the SDOF structure.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.6
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• pp.175-186
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• 1996

In Advanced Vehicle Control System(AVCS), Autonomous Intelligent Cruise Control(AICC) is generally understood to be a system that can be achieved in the near future without the demanding infrastructure components and technoloties. AICC is an automatic vehicle following system with no human engagement in the longitudinal vehicle direction. This paper presents a fuzzy control algorithm to develop the AICC system. The control performance was studied information of vehicles using computer simulations. The most improtant aspects of the work reported here are the adoption of the fuzzy adaptive control law, and the use of filtering concept to reduce the slinky effects that may appear in a formation of vehicles equipped with AICC systems. The simulation results demonstrate the effectiveness of the fuzzy adaptive AICC system and its beneficial effects on traffic flow.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.5
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• pp.550-556
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• 2014

In this study, for the control of a single water tank system, a fuzzy-PID controller design technique based on a fuzzy model is investigated. For this purpose, a water tank system is linearized as a number of submodels depending on the operating point, and a fuzzy model is obtained by fuzzy combining. Each submodel is approximated as a first order time delay model, and a PID controller is designed using several existing tuning techniques. Then, through the fuzzy combination of this controller using the same method as that of the fuzzy model, a fuzzy-PID controller is designed. For the proposed technique, a simulation is performed using the fuzzy model of a water tank system, and the validity is examined by comparing its performance with that of a PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1162-1167
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• 2004

This paper presents the design methodology of digital fuzzy controller(DFC) for the systems with time-delay. We propose the fuzzy feedback controller whose output is delayed with unit sampling period and predicted. The analysis and the design problem considering time-delay become easy because the proposed controller is syncronized with the sampling time. The stabilization problem of the digital fuzzy system with time-delay is solved by linear matrix inequality(LMI) theory. Convex optimization techniques are utilized to solve the stable feedback gains and a common Lyapunov function for designed fuzzy control system. To show the effectiveness the proposed control scheme, the network control example is presented.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2666-2668
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• 2001

Fuzzy system which are based on membership functions and rules, can control nonlinear, uncertian, complex system well. However, Fuzzy controller has problems: It is difficult to design a stable for amateur. To update the then-part membership functions of the fuzzy controller can be designed using the Optimal fuzzy controller. Then we could be optimized the system choosing a good performance index. The proposed fuzzy controller based on Optimal fuzzy control is an Truck-Backer for demonstration of the robustness of proposed methodology.

• Journal of the Korean Society of Industry Convergence
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• v.2 no.1
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• pp.61-67
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• 1999

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. And We designed controller by fuzzy learning method, and then compare fuzzy learning method with LQR. The result of simulations shows that the crane is controlled better than LQR for a very large swing angle of 1 radian within nearly one cycle.