• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.8
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• pp.771-775
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• 2001

In this paper, in order to control uncertain missile autopilot, an adaptive fuzzy control(AFC) scheme via parallel distributed compensation(PDC) is developed for the multi-input/multi -output plants represented by the Takagi-Sugeno(T-S) fuzzy model. Moreover adaptive law is designed so that the plant output tracks the stable reference model(SRM). From the simulations results, we can conclude that the suggested scheme can effectively solve the control problems of uncertain missile systems based on T-S fuzzy model.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.1
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• pp.38-44
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• 2001

This paper investigates the direct adaptive control of nonlinear systems using RBFN(radial basis function networks). The structure of the controller consists of a fixed PD controller and a RBFN controller in parallel. An adaptation law for the parameters of RBFN is developed based on the Lyapunov stability theory to guarantee the stability of the overall control system. The filtered tracking error between the system output and the desired output is shown to be UUB(uniformly ultimately bounded). To evaluate the performance of the controller, the proposed method is applied to the trajectory contro of the two-link manipulator.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.667-673
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• 2000

A number of issues related with the vibrational behavior of pretwisted rotating beams featuring anisotropic properties and incorporating adaptive capabilities are considered in this paper. The adaptive capabilities are provided by a system of piezoactuators bonded or embedded into the structure. Based on the converse piezoelectric effect and on the out of phase activation, boundary control moments are pizoelectrically induced at the beam tip. A feedback control law relating the induced bending moments with the kinematical response quantities appropriately selected is used, and its beneficial effects, considered in conjunction with that of the beam anisotropy and structural pretwist upon the eigenvibration characteristics are highlighted

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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• pp.944-957
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• 2007

In general mechanical servo systems, friction deteriorates the performance of controllers by its nonlinear characteristics. Especially, friction phenomenon causes steady-state tracking errors and limit cycles in position and velocity control systems, even though gains of controllers are tuned well in linear system model. Even if sensor is used higher accuracy level, it is difficult to improve tracking performance of the position to the same level with a general control method such as PID type. Therefore, many friction models were proposed and compensation methods have been researched actively. In this paper, we consider that the variation of mover's mass is various by loading and unloading. The normal force variation occurs by it and other parameters. Therefore, the proposed control system is composed of main position controller and a friction compensator. A parameter estimator for a nonlinear friction model is designed by adaptive control law and adaptive backstopping control method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.603-610
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• 2000

In this paper, the direct and indirect adaptive controller are combined based on the Lyapunov synthesis approach. The Proposed controller is constructed from RBF Neural Network and weighting parameters are adjusted on-line according to some adaptation law. In this scheme, fuzzy IF-THEN rules are used to decide the combined weighting factor. In the results, proposed controller has the main advantages of both the direct adaptive controller and the indirect adaptive controller. The effectiveness of the proposed control scheme is demonstrated through simulation results of control for one-link rigid robotics manipulator.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.9
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• pp.1722-1727
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• 1992

In this study, techniques of adaptive input-output linearizing control of a class of uncertain nonlinear system are investigated. It is shown through concepts of signal growth rates that bounded trackings yield by adaptive input-output linearizing control law using normalized estimator. The convergence characteristics are improved significantly by using the normalized estimator. Simple example is presented as illustration.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.2031-2042
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• 2017

In this paper, a novel traffic flow control method based-on ramp metering and speed regulation using an adaptive sliding mode control (ASMC) method along with a deadzoned parameter adaptation law is proposed at a stochastic macroscopic level traffic environment, where the influence of the density and speed disturbances is accounted for in the traffic dynamic equations. The goal of this paper is to design a local traffic flow controller using both ramp metering and speed regulation based on ASMC, in order to achieve the desired density and speed for the maintenance of the maximum mainline throughput against disturbances in practice. The proposed method is advantageous in that it can improve the traffic flow performance compared to the traditional methods using only ramp metering, even in the presence of ramp storage limitation and disturbances. Moreover, a prior knowledge of disturbance magnitude is not required in the process of designing the controller unlike the conventional sliding mode controller. A stability analysis is presented to show that the traffic system under the proposed traffic flow control method is guaranteed to be uniformly bounded and its ultimate bound can be adjusted to be sufficiently small in terms of deadzone. The validity of the proposed method is demonstrated under different traffic situations (i.e., different initial traffic status), in the sense that the proposed control method is capable of stabilizing traffic flow better than the previously well-known Asservissement Lineaire d'Entree Autoroutiere (ALINEA) strategy and also feedback linearization control (FLC) method.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.422-428
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• 2007

This paper presents a neural network based adaptive control approach to a reconfigurable flight control law that keeps handling qualities in the presence of faults or failures to the control surfaces of an aircraft. This approach removes the need for system identification for control reallocation after a failure and the need for an accurate aerodynamic database for flight control design, thereby reducing the cost and time required to develope a reconfigurable flight controller. Neural networks address the problem caused by uncertainties in modeling an aircraft and pseudo control hedging deals with the nonlinearity in actuators and the reconfiguration of a flight controller. The effect of the reconfigurable flight control law is illustrated in results of a nonlinear simulation of an unmanned aerial vehicle Durumi-II.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.7
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• pp.48-55
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• 1997

An adaptive output feedback controller is designed for tracking control of an n-link robot manipulator with unknown load. High-gain obwserver that is used to estimate joint velocities is designed to avoide the restriction of the allowable variation range of unknown parmeters as well as improve the state estimation error. We saturate the control inut outside a domain of interest and use an adaptive law with a parameter projection feature to guarantee boundedness of all the trajectories in the closed-loop system. Simulation resutls on a 2-link manipulator illustrate that when the speed of the high-gain observer is sufficiently high, the proposed controller recovers the performance under state feedback control.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.724-727
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• 1999

In this paper, the direct and indirect neural adaptive controller are combined based on the Lyapunov synthesis approach. The proposed adaptive controller is constructed from RBF neural network and a set of fuzzy IF-THEN rules. And the weighting parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given trajectory. In this scheme, fuzzy IF-THEN rules are used to decide the combined weighting factor. It is shown that all the signals in the closed-loop system are uniformly bounded under mild assumptions. The effectiveness of the proposed control scheme is demonstrated through the control of one-link rigid robotics manipulator.