• Proceedings of the KIEE Conference
• /
• 1998.07c
• /
• pp.875-879
• /
• 1998

A fault on the transmission line results in the variation of reactance and parametric uncertainties in the power system dynamics. In this case, we need a robust control to cope with these uncertainties. A sliding mode control, a sort of robust control, is known to be robust to parametric or state-dependent uncertainties if the bounding function of uncertain terms is determined a priori. However, in general, we can not readily determine the bounding function for the complex systems. Hence, in this paper we introduce a fuzzy system which can estimate the bounding function in relatively simple way. By the use of the proposed fuzzy system, determination of bounding function is made easier. We applied the proposed scheme to the stabilization of power system under the sudden fault on the transmission lines. The simulation result verifies the effectiveness of the scheme.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.682-684
• /
• 1995

The most effective way to improve the performance of a fuzzy controller may be to optimize look-up values. Look-up values are derived from processes used input-output scale factors, membership functions, rule base, fuzzy inference method and defuzzification. It is powerful way to modify or organize look-up table values. In this paper, We propose the look-up values self-organizing fuzzy controller(LSOFC). We use the plus-minus tuning method(PMTM), scanning values through the processes of addition and subtraction. We show the efficiency of this LSOFC by the results of simulation for nonlinear time-varying plant with unmodelled dynamics.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1410-1413
• /
• 1993

This paper presents the application of fuzzy control to three links of a Rhino robot and compares its performance to traditional PD control. The dynamics of motion of robot links are governed by nonlinear differential equations. The fuzzy controller, being an adaptive technique, gives better performance than the traditional linear PD controller over a typical operational range. The fuzzy controller reaches the desired position with no overshoot, which is unlikely with the PD controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.873-876
• /
• 1993

A fuzzy controller designed by mini-max-gravity(MMG) method is essentially nonlinear with respect to the controller's input and output relationship, and stability analysis is thus needed to construct a stable control system. This paper deals with a design method of a position-type MMG fuzzy controller stable in a sense of Lyapunov when considered is a single-input-single-output linear, stable plant. We first introduce a method to construct a Laypunov function by using an eigen-value of A matrix of the linear, stable plant dynamics and then we derive an asymtotic stability condition in terms of scale factors for fuzzy state variables and controller gain. The stability condition is found reasonably practical through comparing the theoretical stability region with that obtained from simulations.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.1
• /
• pp.159-164
• /
• 2017

In this paper, we propose a method of designing the sampled-data tracking controller for nonlinear systems expressed by the Takagi-Sugeno (T-S) fuzzy model. A sufficient condition that asymptotically stabilizes the state error between the linear reference model and the T-S fuzzy model is derived in terms of linear matrix inequalities. To this end, error dynamics are constructed, and the exact discretization method and the Lyapunov stability theory are employed in this paper. Finally, we validate the proposed method through the simulation example.

• Journal of the Korean Society for Precision Engineering
• /
• v.30 no.8
• /
• pp.824-830
• /
• 2013

We propose a tracking control system for butterfly valves. A sliding mode controller with a fuzzy-neural network algorithm was applied to the design of the tracking control system. The control scheme used the real-time update law for the unmodeled system dynamics using a fuzzy-neural network algorithm. The performance of the proposed control system was assessed through a range of experiments.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.197-197
• /
• 2000

In this paper, we present a novel method for design of fuzzy Logic controllers by defining a set of characteristic parameters (CFLCS). These parameters are extracted and defined by examining the structure and characteristics of the individual components of FLCs and parameterizing those. Based upon this formulation, a series of simulations is conducted for plants having simple dynamics in order to see the performance of this proposed method. The simplicity and effectiveness of this method are discussed in detail.

• Proceedings of the KIEE Conference
• /
• 1997.07b
• /
• pp.693-695
• /
• 1997

This paper deals with the tracking control problem of robotic manipulators with unknown or changing dynamics. The torque input applied to the joint actuators is determined at every instance by the identification model that best approximates the robot dynamics. The best of the identified model is chosen by the proposed switching mechanism with fuzzy inference of the manipulator in an indirect adaptive controller architecture. Simulation results are also included to demonstrate the improvement in the tracking performance when the proposed method is used.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 1995.10a
• /
• pp.96-100
• /
• 1995

In this paper an induction motor control using fuzzy controller and neural network adptive observer is studied. The proposed observer which comprises neural network flux observer which comprises neural network flux observer and neural network torque observer is trained to learn the flux dynamics and torque dynamics and subjected to further on-line training by means of a backpropagation algorithem. Therefore it has been shown that the robust control of induction motor neglects the rotor time constant variations

• Transactions on Control, Automation and Systems Engineering
• /
• v.3 no.2
• /
• pp.117-123
• /
• 2001

To improve control performance of a non-linear system, many other reserches have used the sliding model control algorithm. The sliding mode controller is known to be robust against nonlinear and unmodeled dynamic terms. However, this algorithm raises the inherent chattering caused by excessive switching inputs around the sliding surface. Therefore, in order to solve the chattering problem and improve control performance, this study has developed the sliding mode controller with a perturbation estimator using the observer-based fuzzy adaptive network. The perturbation estimator based on the fuzzy adaptive network generates the control input of compensating unmodeled dynamics terms and disturbance. And the weighting parameters of the fuzzy adaptive network are updated on-line by adaptive law in order to force the estimation errors converge to zero. Therefore, the combination of sliding mode control and fuzzy adaptive network gives rise to the robust and intelligent routine. For evaluation control performance of the proposed approach, tracking control simulation is carried is carried out for the hydraulic motion simulator which is a 6-degree of freedom parallel manipulator.