• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.5-49
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• 2001

The Proportional-Integral-Derivative control scheme is widely used in industries. This paper investigates an alternative control paradigm for controlling lightweight Cartesian robot arms. Fuzzy PI control is used and validated experimentally by comparing performance with a conventional PID control algorithm. The results show the effectiveness of the fuzzy PI control. The fuzzy control shows superior performance in transient response over the conventional one.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2254-2259
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• 2005

In this paper, we propose a wavelet based fuzzy neural network(WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges advantages of neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of mobile robot using the gradient descent(GD) method. In addition, an approach that uses adaptive learning rates for the training of WFNN controller is driven via a Lyapunov stability analysis to guarantee the fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control performance of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.1
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• pp.14-21
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• 2001

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. In indirect adaptive fuzzy control, based on the proved approximation capability of fuzzy systems, they are used to capture the unknown nonlinearities of the plant. Until now, most of the papers in the field of controller design for nonlinear system considers the affine system using fuzzy systems which have fixed grid-rule structure. We proposes a dynamic fuzzy rule insertion scheme where fuzzy rule-base grows as time goes on. With this method, the dynamic order of the controller reduces dramatically and an appropriate number of fuzzy rules are found on-line. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed-loop system is guaranteed.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.472-475
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• 1999

In the paper, we discussed an investigation for Improvement of control precision by reasonably introducing difference and integral of error in Fuzzy control

• The Journal of Korean Association of Computer Education
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• v.7 no.1
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• pp.67-77
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• 2004

In this paper, a new approach to the automation of the cultivation in a green house is suggested and a practical automatic control cultivation system is implemented. To automatically control and optimize the very nonlinear and time-varying growth of farm products, a hybrid strategy(FECA, Fuzzy Expertized Control Algorithm) is proposed which serially combines a fuzzy expert system with the fuzzy logic control. The fuzzy expert system(FMES, Fuzzy Model-based Expert System is intended to overcome the non-linearity of the growth of farm products. The part of fuzzy controller(FLC, Fuzzy Logic Controller) is incorporated to solve the time-variance of the growth of farm products. Finally, the efficiency and the effectiveness of the implemented agricultural automation system is presented through the cultivation results.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.104-111
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• 2000

A fuzzy logic Run-by-Run(RbR) controller and an in -line wafer characteristics prediction scheme for the rapid thermal processing system have been developed for the study of process repeatability. The fuzzy logic RbR controller provides a framework for controlling a process which is subject to disturbances such as shifts and drifts as a normal part of its operation. The fuzzy logic RbR controller combines the advantages of both fuzzy logic and feedback control. It has two components : fuzzy logic diagnostic system and model modification system. At first, a neural network model is constructed with the I/O data collected during the designed experiments. The wafer state after each run is assessed by the fuzzy logic diagnostic system with featuring step. The model modification system updates the existing neural network process model in case of process shift or drift, and then select a new recipe based on the updated model using genetic algorithm. After this procedure, wafer characteristics are predicted from the in-line wafer characteristics prediction model with principal component analysis. The fuzzy logic RbR controller has been applied to the control of Titanium SALICIDE process. After completing all of the above, it follows that: 1) the fuzzy logic RbR controller can compensate the process draft, and 2) the in-line wafer characteristics prediction scheme can reduce the measurement cost and time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.122-129
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• 2008

The direct torque control method of an inverter fed squirrel cage typed induction motor using fuzzy logic controller has been proposed. This method is suitable for the traction which requires a fast torque response during the star-up and step change. The fuzzy control algorithm based upon the control principles of conventional DSC(Direct Self Controller) is developed. The fuzzy algorithm is tarried out by defuzzification strategy of the fuzzy output extracted from the possibility distribution of an inferred fuzzy control rule. The flux and torque of an induction motor are estimated by the dynamic model of the rotor flux field-oriented scheme which has decoupling characteristics and excellent dynamic response over a wide speed range. The proposed controller shows a good dynamic response. Moreover, since the fuzzy controller possesses highly adaptive capability, the performance of fuzzy controller is quite robust and insensitive to the motor parameters and change of operation conditions.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.80-92
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• 1997

This research focuses onan efficient control method of temperature for multiple points using only one processor. For a yarn production system, the surface temperature control of heaters are very important for quality control. Therefore, we designed a temperature controller for a draw and twist machine and applied Fuzzy-PWM algorithm to the controller. If we use a processor for the temperature control of multiple points with the conventional ON/OFF control, the control performance of the system becomes poor. To overcome these problems, we developed a new Fuzzy-PWM algorithm for the adjustment of power rate to the heaters in the conventional ON/OFF control. It is shown that this algorithm has the same effects as the PID algorithm for the temperature control of each point. The proposed algorithm is robust against the production condition and environment such as the reference temperature and the thickness of yarn, since the power rate to the heater is adjusted by Fuzzy Rules derived from the values of the reference termperatureand the thickness of yarn. To obtain optimal Fuzzy rulees, the control simulations are perfodrmed through the modelling of the heater and simulation of Fuzzy rules. This algorithm is applied for the multiple pont temperature controller and showed satisfactory performance.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.1
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• pp.35-47
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• 2004

In this paper presents a new approach to the design and real-time implementation of fuzzy control system based-on digital signal processors(DSP:IMS320C80) in order to improve the precision and robustness for system of industrial robot(Dual-Arm with 8 joint Robot). The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The IMS320C80 is used in implementing real time fuzzy control to provide an enhanced motion control for robot manipulators. In this paper, a Self-Organizing Fuzzy Controller(SOFC) for the industrial robot manipulator with a actuator located at the base is studied. A fuzzy logic composed of linguistic conditional statements is employed by defining the relations of input-output variables of the controller. In the synthesis of a FLC(Fuzzy Logic Controller), one of the most difficult problems is the determination of linguistic control rules from the human operators. To overcome this difficult SOFC is proposed for a hierarchical control structure consisting of basic and high levels that modify control rules. The proposed SOFC scheme is simple in structure, Int in computation, and suitable for implementation of real-time control. Performance of the SOFC is illustrated by simulation and experimental results for a Dual-Arm robot with eight joints.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.272-279
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• 1997

In this paper, a fuzzy generalized predictive control (FGPC) for non-linear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus may possess inherent good properties of the GPC. Computer simulations show that the performance of the FGPC is satisfactory.