• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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• pp.52-54
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• 1996

A number of algorithm using the VSS(Variable Structure System) for uncertain dynamic system are developed. But, in these algorithms, the assumption that the uncertainties are bounded and their bounds are available to the designer is involved. And bounds on the uncertainties are an important clue to guarantee the stability of the closed loop system. However, sometimes bounds on the uncertainties may not be easily obtained because of the complexity of the structure of the uncertainties. Therefore, a methodology by which the boundary values on the uncertainties can be easily obtained is required. The VSS proposed in this proposal employ the new adaptive VSS scheme for uncertain dynamic system being estimated on line. The resulting control law is simple and easy to apply to on line computer control. It can also suppress chattering and maintain good tracking precision even if unmodeled dynamics are considered. And, a new method using nonlinear switching surface is introduced so that the speed response is improved and the good transient response can be obtained. Simulation results are presented and show the advantage of the proposed adaptive VSS with nonlinear switching surfaces.

• 제어로봇시스템학회논문지
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• 제15권4호
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• pp.413-419
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• 2009

A decentralized adaptive control method is proposed for large-scale systems with unknown time-delayed nonlinear interconnections unmatched in control inputs. It is assumed that the time-delayed interaction terms are bounded by unknown nonlinear bounding functions. The nonlinear bounding functions and uncertain nonlinear functions of large-scale systems are compensated by the function approximation technique using neural networks. The dynamic surface control method is extended to design the proposed memoryless local controller for each subsystem of uncertain nonlinear large-scale time delay systems. Therefore, although the interconnected systems consist of a large number of subsystems, the proposed controller can be designed simply. We prove that all the signals in the total closed-loop system are semiglobally uniformly bounded and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is given to demonstrate the effectiveness and applicability of the proposed scheme.

• Transactions on Control, Automation and Systems Engineering
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• 제3권4호
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• pp.262-267
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• 2001

Jeha Ryu Department of Mechatronics, Kwangju Institute of Science and Technology This paper presents an application of a robust adaptive control strategy to HexaSlide type six degrees-of-freedom parallel manipulators. The HexaSlide type parallel manipulators are characterized as an architecture with constant link lengths that are attached to moving sliders on the ground and to a mobile platform. The proposed control law is developed based on a simplified second order system dynamic equation in joint space with uncertain mass, damper, spring, and Coulomb friction terms. These uncertain parameters are updated by an adaptation law that is derived by Lyapunov stability theorem. A robust adaptive control law by using the boundary layer is designed for the purpose of compensating for the neglected dynamic effects of the mobile platform and the six moving links that are modeled as a disturbance term. Experimental results show good and fast tracking performance.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권4호
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• pp.215-219
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• 2005

This paper aims at asymptotic stabilization for uncertain dynamic systems with state and input delays. We propose a memoryless state feedback controller which maximizes the delay bound for guaranteeing stability of the system. Using Lyapunov method and linear matrix inequality (LMI) approach, a delay-dependent stabilization criterion is devised by taking the relationship between the terms in the Leibniz-Newton formula into account. The criterion is represented in terms of LMIs, which can be solved by various efficient convex optimization algorithms. Numerical examples are given to illustrate our main method.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권7호
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• pp.418-423
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• 2003

In order to describe the upper bound of the uncertainties without any information of the structure, we assume that the upper bound is represented as a Fredholm integral equation of the first kind, that is, an integral of the product of a predefined kernel with an unknown influence function. Based on the improved Lyapunov function, we propose an adaptation law that is capable of estimating the upper bound and we design a sliding mode control, which controls effectively for uncertain dynamic systems.

• 제어로봇시스템학회논문지
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• 제14권8호
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• pp.792-799
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• 2008

Motion estimation is crucial in a remote control for its convenience or accuracy. Time delays, however, can occur in the problem because data communication is required through a network. In this paper, state estimation problem with uncertain time delayed measurements is addressed. In dynamic system with noise, after taking measurements, it often requires some time until that is available in the filter algorithm. Standard filters not considering this time delays cannot be used since the current measurement is related with a past state. These delayed measurements are solved with augmented extended Kalman filter, and the uncertainty of delayed time is also resolved based on an explicit formulation. The proposed method is analyzed and verified by simulations.

• Structural Engineering and Mechanics
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• 제23권6호
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• pp.599-613
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• 2006

A method for the dynamical analysis of FE discretized uncertain linear and nonlinear structures is presented. This method is based on the moment equation approach, for which the differential equations governing the response first and second-order statistical moments must be solved. It is shown that they require the cross-moments between the response and the random variables characterizing the structural uncertainties, whose governing equations determine an infinite hierarchy. As a consequence, a closure scheme must be applied even if the structure is linear. In this sense the proposed approach is approximated even for the linear system. For nonlinear systems the closure schemes are also necessary in order to treat the nonlinearities. The complete set of equations obtained by this procedure is shown to be linear if the structure is linear. The application of this procedure to some simple examples has shown its high level of accuracy, if compared with other classical approaches, such as the perturbation method, even for low levels of closures.

• 전자공학회논문지B
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• 제31B권8호
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• pp.64-73
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• 1994

In this paper we present an algorithmic technique for determining a feedback compensator which will stabilize the interval dynamic system specifically the robust regulator design for interval plants. The approach taken here is to allow the system parameters to live within prescribed intervals then design a dynamic feedback compensator which guarantees closed-loop system stable. The main contribution of this paper is the idea of introducting a "simplified Kharitonov`s results" for low order polynomials to search for suitable compensator parameters in the compensator parammeter space to make the uncertain system robust. We also design the robust regulator which will $D_{\phi}$ -stabilize (have the closed-loop poles in the left sector only) the dynamic interval system while having good performance. the numerical examples are given to show the substantially improved robustness which results from our approach.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.168-172
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• 1996

In master-slave teleoperating system, it is important that the system has good maneuverability. In this paper, it is addressed an adaptive learning control method applicable to the master-slave system. This control scheme has the ability to estimate uncertain dynamic parameters included intrinsically in the system and to achieve the desired performance without the nasty matrix operation. The proposed method is applied to a master-slave teleoperating system composed of two SCARA robots and verified experimentally.

• 대한전기학회논문지:전력기술부문A
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• 제48권12호
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• pp.1520-1526
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• 1999

This paper presents a current feedback learning controller for dynamic control of DC motors. The proposed controller uses the full third-order dynamics model of DC motor system to drive stable learning rules for virtual current learning input, voltage learning input, and the coefficient of electromotive force. It is shown that the proposed learning controller drives the state of uncertain DC motor system with unknown system parameters and external load torque to the desired one globally asymptotically. Computer simulation and experimental results are given to demonstrate the effectiveness of the proposed adaptive learning controller.