• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.149-151
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• 2001

This paper proposes the design of STATCOM(static synchronous compensator) stabilizer for improving power system stability using fuzzy logic controller(FLC). The STATCOM DC voltage regulator contributes negative damping to the power system as the installation of STATCOM DC voltage regulator. STATCOM stabiliser is superimposed on the AC voltage regulator to compensate the negative damping effect. To evaluate usefulness of the proposed method, we perform the nonlinear simulation on a single-machine infinite bus system. As results of the simulations, the proposed method shows better control performance than PI controller in terms of damping effects.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.10
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• pp.59-68
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• 1992

In this paper, we proposed coordinator description and ambiguity on the hybrid controller for position/force control of robot manipulator. When the hybrid controller is desiged based on the PID control conception, the parameter sharing problem must be considered. However, selection problem of coordinate system on n-DOF robot manipulator control is unsolved. Moreover, contact force on object and change of shape make another problems. And it is very difficult to figure out the accurate mathematical model of manipulator on account of ambiguity and nonlinearity of actuator. Therfore, we design a new hybrid controller, FPID(Fuzzy PID). For verifying the validity of the controller, we tried computer simulation of this system. As a result, we can get remarkable improvement of overdamping and overshooting. Also we can solve compicance problem effectively. Furthermore, ambiguity problem is solved by adding control knowledge based compensator. So robust controller can be acheived, too.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.185-191
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• 2008

In this paper, experimental studies of a neural network (NN) control technique for non-model based position control of the x-y table robot are presented. Decentralized neural networks are used to control each axis of the x-y table robot separately. For an each neural network compensator, an inverse control technique is used. The neural network control technique called the reference compensation technique (RCT) is conceptually different from the existing neural controllers in that the NN controller compensates for uncertainties in the dynamical system by modifying desired trajectories. The back-propagation learning algorithm is developed in a real time DSP board for on-line learning. Practical real time position control experiments are conducted on the x-y table robot. Experimental results of using neural networks show more excellent position tracking than that of when PD controllers are used only.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.640-647
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• 2007

This paper presents a joint controller for a humanoid robot based on genetic algorithm. h humanoid robot has basically instability during walking because it isn't fixed on the ground. Moreover nonlinearities of the joints increase its instability. If one of them isn't satisfied, the robot may fall down at the ground during walking. To attack one of those problems, joint controller is proposed. It can perform tracking control preciously and reduce the effect of nonlinearities by gear, limitation of the input voltage, coulomb friction and so on. This controller is based on fuzzy-sliding mode controller (FSMC) and compensator and control gains are searched by a proposed genetic algorithm. It can reduce the effect by nonlinearities. Also, to improve the tracking performance, the proposed controller has motion controller. From the given controller, a humanoid robot can moved more preciously. Here, all the processes are investigated through simulations and it is verified experimentally in a real joint system for a humanoid robot.