• 전자공학회논문지SC
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• 제37권3호
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• pp.19-32
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• 2000

본 논문이 제안하는 제어기는 새로운 가변 경계층을 이용하여 가변 구조 시스템의 정확한 추적 제어가 이루어지게 한다. 지금까지 가변 경계층을 적용한 가변 구조 제어기는 고정된 슬라이딩면의 사용으로 초기 위치가 제한되었으며 이로 인해 임의의 초기 위치에서부터 시스템 상태 궤적의 채터링을 감소시킬 수 없다. 그러나 제안된 제어기는 선형 시변 슬라이딩면을 이용함으로서 모든 상태로부터 외부 외란과 불확실한 매개변수에 대한 강인한 특성을 지니게 되며 새로운 가변 경계층의 적용으로 채터링과 추적 성능에서 기존 방법에 비해 우수한 특성을 보이고 있다. 따라서 본 논문은 2 관절 매니퓰레이터에 제안된 알고리즘을 적용하여 제시한 방법의 타당성을 입증하였다.

• 제어로봇시스템학회논문지
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• 제3권3호
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• pp.272-279
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• 1997

In this paper, a fuzzy generalized predictive control (FGPC) for non-linear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus may possess inherent good properties of the GPC. Computer simulations show that the performance of the FGPC is satisfactory.

• 대한전기학회논문지
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• 제32권11호
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• pp.394-403
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• 1983

본논문은 행렬비로 기미되는 다입.출력 연속시간 시스템에 대한 기준 모델형 적응제어에 관하여 고찰한다. 제어기는 monopoli - Narendra type 으로서 파라메타 적응칙에 시변리득행렬을 도입하였으며 가조정 제어기를 포함한 플랜트의 전달함수 행렬이 기준모델의 그것에 점차 따라가도록 한다. interactor 행렬에 대한 지식을 비롯한 약간의 가정하에서 단일 입.출력 시스템의 경우의 알고리즘이 적절하게 적용될 수 있음을 보인다. 적응칙의 수렴성은 안정도 이론을 이용하여 증명하며 전체 시스템의 안정성은 해석적인 고찰에 의해 보여 준다.

• Journal of Power Electronics
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• 제14권5호
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• pp.1008-1027
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• 2014

An electric scooter with a V-belt continuously variable transmission (CVT) driven by a permanent magnet synchronous motor (PMSM) has a lot of nonlinear and time-varying characteristics, and accurate dynamic models are difficult to establish for linear controller designs. A PMSM servo-drive electric scooter controlled by a novel hybrid modified recurrent Legendre neural network (NN) control system is proposed to solve difficulties of linear controllers under the occurrence of nonlinear load disturbances and parameters variations. Firstly, the system structure of a V-belt CVT driven electric scooter using a PMSM servo drive is established. Secondly, the novel hybrid modified recurrent Legendre NN control system, which consists of an inspector control, a modified recurrent Legendre NN control with an adaptation law, and a recouped control with an estimation law, is proposed to improve its performance. Moreover, the on-line parameter tuning method of the modified recurrent Legendre NN is derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, two optimal learning rates for the modified recurrent Legendre NN are derived to speed up the parameter convergence. Finally, comparative studies are carried out to show the effectiveness of the proposed control scheme through experimental results.

• International Journal of Control, Automation, and Systems
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• 제3권3호
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• pp.397-402
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• 2005

The guaranteed cost control problem for a class of linear systems with norm-bounded time-varying parameter uncertainties and input constraints is considered. A sufficient condition for the existence of guaranteed cost state feedback controllers is derived via the linear matrix inequality (LMI) approach, and a design procedure to guaranteed cost controllers is given. Furthermore, a convex optimization problem is formulated to determine the optimal guaranteed cost controller. An example is given to illustrate the effectiveness of the proposed results.

• 한국정밀공학회지
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• 제23권6호
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• pp.81-87
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• 2006

A new gain-scheduled control design is proposed to improve disturbance attenuation for systems with bounded control input. The state feedback controller is scheduled according to the proximity to the origin of the state of the plant. The controllers is derived in the framework of linear matrix inequality(LMI) optimization. This procedure yields a linear time varying control structure that allows higher gain and hence higher performance controllers as the state move closer to the origin. The main results give sufficient conditions for the satisfaction of a parameter-dependent performance measure, without violating the bounded control input condition.

• 전기학회논문지
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• 제67권2호
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• pp.308-313
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• 2018

In this paper, we propose a sampled-data model predictive tracking control deign for leader-following control of multi-mobile robot system. The error dynamics of leader-following robots is modeled as a Linear Parameter Varying (LPV) model. Also, the Lyapunov function is presented to guarantee stability of the networked control system. Based on the stabilization condition using a quadratic Lyapunov function approach, model predictive sampled-data controller is designed. Finally, the leader-following control of multi mobile robots is simulated to show effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.498-503
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• 1993

In this paper, a robust and reliable H$_{\infty}$ control problem is considered for linear uncertain systems with time-varying norm-bounded uncertainty in the state matrix, which performs well despite of actuator outages. Using linear static state feedback and the quadratic stabilization with H$_{\infty}$-norm bound, a robust and reliable H$_{\infty}$ controller is obtained that stabilizes the plant and guarantees an H$_{\infty}$-norm bound constraint on disturbance attenuation for all admissible uncertainties and normal state as well as faulty state of actuators. It provides a sufficient condition for robust and reliable stabilization with H$_{\infty}$-norm bound. Reliability is guaranteed provided actuator outages only occur within a prespecified subset of actuators.tors.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.66-68
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• 1997

In this paper, our interest is the identification and control of nonlinear dynamic plant, induction motor, by using neural networks. We usually use vector control in the induction motor such as in the DC motor. When we go over the inputs of voltage source invertor, we can find that torque current and flux current couple each other in the induction motor. Before putting control inputs in the system, we should remove the coupling terms which we already know from them. But we should consider that cross coupling terms have time-varying variables. In this paper, we identified the parameter of induction motor by using neural networks and designed the controller with identified parameters. Through this procedure we obtained compensated inputs which are decoupled each other. Using induction motor currents control, we can make the d axis current hold constant value and control the q axis current at the same time.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.410-410
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• 2000

Impedance control is recognised as one of the most proper control scheme to carry out the assembly tasks, since it can control the dynamic relationship between the manipulator and environment directly. However, it is well known that the contact force cannot be controlled directly using the impedance control. Also impedance parameters should be properly defined depending on the task to be performed. We propose a new position based impedance control, which has self-adjusted impedance parameters and can control the contact force explicitly, Impedance parameters, as time-varying parameters, are adjusted automatically based on the measured contact force and the position error during the task. A proposed algorithm was implemented on the peg-in-hole task with the industrial manipulator. We shows the effectiveness of proposed control method experimentally.