• Proceedings of the KIPE Conference
• /
• 2003.07b
• /
• pp.584-587
• /
• 2003

Iterative learning control is an approach to improve the transient response of systems that operate repetitively over a fixed time interval. It is useful for the system where the system output follows the different type input, in case of design or modeling uncertainty In this paper, we introduce the concept of iterative learning control and then apply the learning control algorithm for multi-motor system for performance Improvement.

• Journal of Industrial Technology
• /
• v.22 no.A
• /
• pp.67-72
• /
• 2002

A technique for alignment to true north is presented, based on synchronized measurements of vision image by a camera and output voltage of wind direction sensor. The true wind direction is evaluated by means of image processing techniques with least square sense, and then evaluated true value is compared with measured output voltage of the sensor. The proposed technique is applied to real meteorological tower m Daekwanryung test site. In addition, some uncertainty analysis of this method is presented.

• Proceedings of the KIPE Conference
• /
• 2001.10a
• /
• pp.443-447
• /
• 2001

In this paper, a robust sensorless vector control system for induction motors with a speed estimator and an uncertainty observer is presented. At first, the proposed speed estimator is based on the MRAS(Mode Reference Adaptive System) scheme and constructed with a simple fuzzy logic(FL) approach. The structure of the proposed FL estimator is very simple. The input of the FL is the rotor flux error difference between reference and adjustable model, and the output is the estimated incremental rotor speed Secondly, the unmodeled uncertainties such as parametric uncertainties and external load disturbances are modeled by a radial basis function network(RBFN). In the overal speed control system, the control inputs are composed with a norminal control input and a compensated control input, which are from RBFN observer output and the modeling error of the RBFN, repectively. The compensated control input is derived from Lyapunov unction approach. The simulation results are presented to show the validity of the proposed system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.517-521
• /
• 1998

DEA(data envelopment analysis) is a non-parametric technique for measuring and evaluating the relative efficiencies of a set of entities with common crisp inputs and outputs. In fact, in a real evaluation problem input and output data of entities often flucturate. These fluctuating data can be represented as linguistic variables characterized by fuzzy numbers. Based on a fundamental CCR model, a fuzzy DEA model is proposed to deal with fuzzy input and output data, Furthermore, a model that extends a fuzzy DEA to a more general case is also proposed with considering the relation between DEA and RA (regression analysis) . the crisp efficiency in CCR modelis extended to an L-R fuzzy number in fuzzy DEA problems to reflect some uncertainty in real evaluation problems.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.2 s.173
• /
• pp.290-302
• /
• 2000

In this paper, we study on the identification of discrete-time state-space model for robust control of tandem cold mill, using a MOESP(MIMO output-error state-space model identification) algorithm based on subspace method. It is shown that the identified model is well adapted to input-output data sets, which are obtained from nonlinear mathematical equations of tandem cold mill. Furthermore, deterministic H$\infty$ norm bounds on uncertainties including modeling errors and disturbances are quantitatively identified in the frequency domain. Finally, the results give a basic idea to determine weighting functions included in formulating some robust control problems of tandem cold mill.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.995_996
• /
• 2009

This paper presents a sliding mode control method for step up DC-DC converter. For high performance control of converter, it requires the robustness between the input current and the output voltage. As a result, in spite of disturbance and parameter uncertainty, the proposed controller has the robustness to control the output voltage.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.15 no.5
• /
• pp.72-78
• /
• 2006

The paper proposes reference model error feedback control scheme for motion control system with hard non-linear components as like saturation and dead-zone in plant input part. Additionally, the plant has the system uncertainty effected by plant model parameter deviation and disturbance. The control algorithm uses the reference model to apply additional feedback loop with the error between reference model output and actual output effected by disturbance and non-linear components. And the stability evaluation based on Popov stability and controller design method are formulated to be performed. The effectiveness of the proposed scheme is examined by simulations. The results are proven by reasonable performances following reference model responses with good disturbance rejection performance without over-tuning of controller.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.177-182
• /
• 1994

In this paper, an output feedback adaptive variable structure control scheme is presented for stabilization of large scale power systems. An additional input signal which is called a power system stabilizer(PSS) is needed to improve the stability of a power system and to maintain the synchronization of generators. The proposed PSS scheme does not require a priori knowledge of uncertainty bounds. It is guaranteed that the closed-loop system is globally uniformly ultimately bounded by the Lyapunov stability theory. Simulation results for a multimachine power system are given to show the feasibility of the proposed scheme and the superiority of the proposed PSS in comparison with the conventional lead-lag PSS of PID-type.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.8
• /
• pp.822-827
• /
• 2014

This paper presents a robust observer-based output feedback control for stabilization of linear time invariant systems with polytopic uncertainties. To this end, this paper not only finds a robust observer gain but also suggests how to determine the model used in the observer, which is not obvious due to model uncertainties in the conventional observer design method. The robust observer gain and the observer model are selected in a way that the whole closed-loop is stable by solving LMIs and BMIs (Linear Matrix Inequalities and Bilinear Matrix Inequalities). A simulation example shows that the proposed robust observer-based output feedback control successfully leads to closed-loop stability.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.341-345
• /
• 1997

A novel fuzzy basis function vector-based adaptive control approach for Multi-input and Multi-output(MIMO) system is presented in this paper, in which the nonlinear plants is first linearised, the fuzzy basis function vector is then introduced to adaptively learn the upper bound of the system uncertainty vector, and its output is used as the parameters of the compensator in the sense that both the asymptotic error convergence can be obtained for the colsed loop nonlinear control system.