• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.144-155
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• 2004

This paper addresses the issue of position control of a chain of multiple mass-spring-damper (CMMSD) units which can be found in many physical systems. The dynamic model of a CMMSD system with any degrees of freedom is expressed in a closed-form for the convenience of the controller design. Backstepping and model reference adaptive control (MRAC) approaches are then used to develop two adaptive output feedback controllers to control the position of a CMMSD system. The proposed controllers rely on the measurements of the input (force) and the output (position of the mass unit at the end of the chain) of the system without the knowledge of its parameters and internal states. Simulations are used to verify the effectiveness of the controllers

• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.526-528
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• 2006

An adaptive output-feedback neural control problem of SISO strict-feedback nonlinear system is considered in this paper. The main contribution of the proposed method is that it is shown that the output-feedback control of the strict-feedback system can be viewed as that of the system in the normal form. As a result, proposed output-feedback control algorithm is much simpler than the previous backstepping-based controllers. Depending heavily on the universal approximation property of the neural network (NN) only one NN is employed to approximate lumped uncertain nonlinearity in the controlled system.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.51-60
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• 2007

This paper describes a visual tracking control law of an Unmanned Aerial Vehicle(UAV) for monitoring of structures and maintenance of bridges. It presents a control law based on computer vision for quasi-stationary flights above a planar target. The first part of the UAV's mission is the navigation from an initial position to a final position to define a desired trajectory in an unknown 3D environment. The proposed method uses the homography matrix computed from the visual information and derives, using backstepping techniques, an adaptive nonlinear tracking control law allowing the effective tracking and depth estimation. The depth represents the desired distance separating the camera from the target.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.12
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• pp.518-525
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• 2006

This paper presents the adaptive robust control method for the flight control systems with model uncertainties. The proposed control system can be composed simply by a combination of the adaptive dynamic surface control (DSC) technique and the self recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides us with the ability to overcome the 'explosion of complexity' problem of the backstepping controller. The SRWNNs are used to observe the arbitrary model uncertainties of flight systems, and all their weights are trained on-line. From the Lyapunov stability analysis, their adaptation laws are induced and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a high performance aircraft (F-16) are utilized to validate the good tracking performance and robustness of the proposed control system.

• Journal of Institute of Convergence Technology
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• v.5 no.2
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• pp.13-19
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• 2015

Recently, quadcopters have been popular as aerial drones. The structure of quadcopters is simpler than traditional helicopters and they are easy to construct and maneuver. Various hardware platforms for quadcopters have been developed. However, the controller design is not easy due to the requirement of 6-DOF flights using 4 rotors(control inputs)(under-actuated systems). In order to overcome the underactuation problem, various control methods - PID, LQR, $H_{\infty}$, SMC, backstepping control, and etc. - have been suggested for the control of quadcopters. In this paper, dynamic features and control methods of quadcopters are reviewed and evaluated. Future works are proposed for designing the advanced controllers of quadcopters.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.856-861
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• 2011

The mobile robot controller is designed to track the target and to maintain the formation at the same time. Formation control is included in mobile robot controller by extending the trajectory tracking algorithm. The dynamic model of mobile robot is used with kinematic model considering the practical physical parameters of mobile robot. The dynamic model of mobile robot transforms velocity control input of kinematic model into torque control input which is the practical control input of mobile robot. Formation controller of mobile robot is designed to satisfy Lyapunov stability by backstepping method. The designed formation controller is applied to the mobile robot for various target movements and simulated to confirm the Lyapunov stability.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1848-1849
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• 2006

The new robust controller design method is proposed for the flight control systems with model uncertainties. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides us with the ability to overcome the "explosion of complexity" problem of the backstepping controller. The SRWNNs are used to observe the arbitrary model uncertainties of flight systems and all their weights are trained on-line. From the Lyapunov stability analysis, their adaptation laws are induced and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a high performance aircraft (F-16) are utilized to validate the good tracking performance and robustness of the proposed control system.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1505-1506
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• 2008

In this paper, a robust adaptive backstepping control is developed for efficiency optimization of induction motors with uncertainties. The proposed control scheme consists of efficiency flux control(EFC) using a sliding mode adaptive flux observer and robust speed control(RSC) using a function approximation for mechanical uncertainties. In EFC, it is important to find the flux reference to minimize power losses of induction motors. Therefore, we proposed the optimal flux reference using the electrical power loss function. The sliding mode flux observer is designed to estimate rotor fluxes and variation of inverse rotor time constant. In RSC, the unknown function approximation technique employs nonlinear disturbance observer(NDO) using fuzzy neural networks(FNNs). The proposed controller guarantees both speed tracking and flux tracking. Simulation results are presented to illustrate the effectiveness of the approaches proposed.

• Proceedings of the KIEE Conference
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• 1997.07a
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• pp.115-117
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• 1997

본 논문에서는 유도전동기의 적응 비선형 제어에 대해 논한다. 제어 목적은 회전자저항의 불확실성에도 불구하고 유도전동기의 속도 및 자속을 분리하여 제어하는 것이며, 이를 위해 백스테핑(Backstepping) 기법을 사용한 비선형제어기와 회전자 저항 추정기를 결합한다. 제안된 적응제어시스템은 내부의 모든 변수가 유계(Bounded)되어 있다는 점에서 안정하며, 더불어 그 개선된 성능을 모의실험을 통해 보인다.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.223-233
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• 2008

A new output feedback controller design approach for flexible-joint (FJ) robots via the observer dynamic surface design technique is presented. The proposed approach only requires the feedback of position states. We first design an observer to estimate the link and actuator velocity information. Then, the link position tracking controller using the observer dynamic surface design procedure is developed. Therefore, the proposed controller can be simpler than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop system are uniformly ultimately bounded. Finally, the simulation results of a three-link FJ robot are presented to validate the good position tracking performance of the proposed control system.