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

• 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.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.283-294
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• 2007

In this paper, a nonlinear controller based on adaptive sliding-mode method which has a sliding surface vector including new boundizing function is proposed and applied to a two-wheeled welding mobile robot (WMR). This controller makes the welding point of WMR achieve tracking a reference point which is moving on a smooth curved welding path with a desired constant velocity. The mobile robot is considered in view of a kinematic model and a dynamic model in Cartesian coordinates. The proposed controller can overcome uncertainties and external disturbances by adaptive sliding-mode technique. To design the controller, the tracking error vector is defined, and then the sliding surface vector including new boundizing function and the adaptation laws are chosen to guarantee that the error vector converges to zero asymptotically. The stability of the dynamic system is shown through the Lyapunov method. In addition, a simple way of measuring the errors by potentiometers is introduced. The simulations and experimental results are shown to prove the effectiveness of the proposed controller.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.3
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• pp.1-8
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• 2013

An application of adaptive flight controller is required for the non-linear and high uncertain system that configuration of tiltrotor aircraft is dramatically changed from rotary wing mode to fixed wing mode. In this paper, the applicable adaptive controller for the tiltrotor aircraft was designed using Neural Networks and DMI (Dynamic Model Inversion). The performance of the SCAS (Stability and Control Augmentation System) was simulated against manned military specification, using the fullscale model of 'Smart UAV(Unmanned Aerial Vehicle)' developed by Korea Aerospace Research Institute. And Neural Networks based adaptive controller was verified through its whole operating envelope using the established HQ (Handling Quality) criteria.

• Smart Structures and Systems
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• v.18 no.6
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• pp.1169-1188
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• 2016

Real-time substructuring techniques are currently an advanced experimental method for testing large size specimens in the laboratory. In dynamic substructuring, the whole tested system is split into two linked parts, the part of particular interest or nonlinearity, which is tested physically, and the remanding part which is tested numerically. To achieve near-perfect synchronization of the interface response between the physical specimen and the numerical model, a good controller is needed to compensate for transfer system dynamics, nonlinearities, uncertainties and time-varying parameters within the physical substructures. This paper presents the substructuring approach and control performance of the linear and the adaptive controllers for testing the dynamic characteristics of soil-structure-interaction system (SSI). This is difficult to emulate as an entire system in the laboratory because of the size and power supply limitations of the experimental facilities. A modified linear substructuring controller (MLSC) is proposed to replace the linear substructuring controller (LSC).The MLSC doesn't require the accurate mathematical model of the physical structure that is required by the LSC. The effects of parameter identification errors of physical structure and the shaking table on the control performance of the MLSC are analysed. An adaptive controller was designed to compensate for the errors from the simplification of the physical model in the MLSC, and from parameter identification errors. Comparative simulation and experimental tests were then performed to evaluate the performance of the MLSC and the adaptive controller.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.366-369
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• 2005

Background subtraction is mainly used for the real-time extraction and tracking of moving objects from image sequences. In the outdoor environment, there are many changeable factor such as gradually changing illumination, swaying trees and suddenly moving objects, which are to be considered for the adaptive processing. Normally, GMM(Gaussian Mixture Model) is used to subtract the background adaptively considering the various changes in the scenes, and the adaptive GMMs improving the real-time performance were worked. This paper, for on-line background subtraction, applied the improved adaptive GMM, which uses the small constant for learning rate ${\alpha}$ and is not able to speedily adapt the suddenly movement of objects, So, this paper proposed and evaluated the dynamic control method of ${\alpha}$ using the adaptive selection of the number of component distributions and the global variances of pixel values.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.72-81
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• 2009

This paper presents a robust adaptive control method using model reference control strategy against autonomous robot systems with random friction nature. We approximate a nonlinear robot system model by means of a feedback linearization approach to derive nominal control law. We construct a Least Square (LS) based observer to estimate friction dynamics online and then represent a perturbed system model with respect to approximation error between an actual friction and its estimation. Model reference based control design is achieved to implement an auxiliary control in order for reducing control error in practice due to system perturbation. Additionally, we conduct theoretical study to demonstrate stability of the perturbed system model through Lyapunov theory. Numerical simulation is carried out for evaluating the proposed control methodology and demonstrating its superiority by comparing it to a traditional nominal control method.

• Journal of Korea Multimedia Society
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• v.21 no.10
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• pp.1182-1194
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• 2018

The Dynamic Adaptive Streaming over HTTP(DASH) is envisioned to evolve to meet an increasing demand on providing seamless video streaming services in the near future. The DASH performance heavily depends on the client's adaptive quality selection algorithm that is not included in the standard. The existing conventional algorithms are basically based on a procedural algorithm that is not easy to capture and reflect all variations of dynamic network and traffic conditions in a variety of network environments. To solve this problem, this paper proposes a novel quality selection mechanism based on the Deep Q-Network(DQN) model, the DQN-based DASH Adaptive Bitrate(ABR) mechanism. The proposed mechanism adopts a new reward calculation method based on five major performance metrics to reflect the current conditions of networks and devices in real time. In addition, the size of the consecutive video segment to be downloaded is also considered as a major learning metric to reflect a variety of video encodings. Experimental results show that the proposed mechanism quickly selects a suitable video quality even in high error rate environments, significantly reducing frequency of quality changes compared to the existing algorithm and simultaneously improving average video quality during video playback.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.5
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• pp.393-400
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• 2002

Teleoperation enables an operator to manipulate remote objects. One of the main goals in teleoperation researches is to provide the operator with the fueling of the telepresence, being present at the remote site. For these purposes, a master robot must be designed as a bilateral control system that can transmit position/force information to a slave robot and feedback the interaction force. A newly proposed impedance algorithm is applied for the control of a haptic interface that was developed as a master robot. With the movements of the haptic interface for position/force commands, impedance parameters are always varying. When the impedance parameters between an operator and the haptic interface and the dynamic model are known precisely, many model based control theories and methods can be used to control the device accurately. However, due to the parameters'variations and the uncertainty of the dynamic model, it is difficult to control haptic interfaces precisely. This paper presents a robust adaptive impedance control algorithm for haptic interfaces.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1962-1964
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• 2006

In this paper, the dynamic models of SOFC are suggested. It consists of electrochemical model, thermal model, voltage equation and several loss equations. Control problems on tracking steady voltage by air flow is discussed and an adaptive controller is designed to withstand to the variation of stack current. Simulation is done to prove the solution of control algorithms.