• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1883-1887
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• 1991

Identification theory for linear discrete system has been presented by a great many reference, but research works for identification of continuous-time system are less than preceding identification. In fact, a great man), systems for engineering are continuous-time systems, hence, research for identification of continuous-time system has important meaning. This paper offers the following results: 1. Corresponding relations for the parameters of continuous-time model and discrete model may be shown, when single input-output system has general characteristic roots. 2. To do identification of single variable continuity linear system with stability constraints from samples of input-output data, it is necessary to use optimization with stability constraints. 3. Main results of this paper may be explained by a simple example.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.327-330
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• 1996

It is well known that the controller output limits have a signifiant effect on the closed loop system performance. Considering the input constraints in GPCF, an effective selection method of the control weighting(.gamma.) is proposed to reduce the amplitude and the rate of control signals so that control signals lie within the limits. It is based on the relation between control weighting(.gamma.) and optimal solution of the unconstrained GPCF. The GPCFIC algorithm chooses an .gamma. at each sampling time so that all unconstrained GPCF output over the control horizon satisfy the rate and the amplitude constraints. In order to evaluate the performance of the GPCFIC, the computer simulations have been done for level control of PWR steam generator in low power operation and shown satisfactory results.

• Transactions on Control, Automation and Systems Engineering
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• 제2권4호
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• pp.221-227
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• 2000

This paper combines the constrained model predictive control with the feedback linearization to solve a nonlinear system control problem with input constraints. The combined approach consists of two steps: Firstly, the nonlinear model is linearized by the feedback linearization. Secondly, based on the linearized model, the constrained model predictive controller is designed taking input constraints into consideration. The proposed controller is applied to two link robot system, and tracking performances of the controller are investigated via some simulations, where the comparisons are done for the cases of unconstrained, constrained input in feedback linearization.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 A
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• pp.335-338
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• 2003

In this paper, we considered the design of gain scheduled controllers for linear systems with input constraints. The gain scheduled control is a method that uses larger control gain when the states are smaller, and smaller gain when it is larger. By doing this, we can use a full actuator capacity. Also we allow the over-saturation in control to improve the performance. First, we derive a control and a reachable set expressed as LMI form, while minimizing the $L_2$ gain from the disturbance to the measured output. Next, the reachable set is divided as nested subsets, and the control gains are obtained by minimizing the $L_2$ gain at each nested subset. Finally, the control gains are scheduled according to the status of states, i.e., the nested-subset in which the states are located. Performance of the proposed technique is illustrated through simulations of a six-story building subject to earthquake ground motion.

• 제어로봇시스템학회논문지
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• 제3권6호
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• pp.582-587
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• 1997

This paper deals with the control problem under nonlinear boiler systems with noise, and input constraints. MCMBPC(Multivariable Constrained Model-Based Predictive Controller) proposed by Wilkinson et al.[10,11] is used and nominal model is modified in this paper in order to applied to nonlinear boiler systems with feed-forward terms. The solution of the cost function optimization constrained on input and/or output variables is achieved using quadratic programming, via singular value decomposition(SVD). The controller designed is shown to satisfy the constraints and to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.160-170
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• 2008

Temperature control of an enclosed thermal system which has many applications including Rapid Thermal Processing (RTP) of semiconductor wafers showed an input-constraint violation for nonlinear controllers due to inherent strong coupling between the elements [1]. In this paper, a constrained nonlinear optimal control design is developed, which accommodates input constraints using the linear algebraic equivalence of the nonlinear controllers, for the temperature control of an enclosed thermal process. First, it will be shown that design of nonlinear controllers is equivalent to solving a set of linear algebraic equations-the linear algebraic equivalence of nonlinear controllers (LAENC). Then an input-constrained nonlinear optimal controller is designed based on that LAENC using the constrained linear least squares method. Through numerical simulations, it is demonstrated that the proposed controller achieves the equivalent performances to the classical nonlinear controllers with less total energy consumption. Moreover, it generates the practical control solution, in other words, control solutions do not violate the input-constraints.

• 한국정보통신학회논문지
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• 제22권1호
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• pp.9-16
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• 2018

예측제어는 제어대상의 예측모델을 이용하여 기준명령과 상태의 미래 오차를 예측하고 최소화시키는 현재 입력을 구하며, 상태와 입력의 제한조건도 고려할 수 있는 매우 실용적인 방법이다. 이동로봇에 대해 예측제어가 적용된 연구들이 있었으나 제어성능을 결정하는 여러 제어 파라미터들이 임의로 지정됨에 따라 성능이 최적화되지 못하였다. 본 논문에서 입력제한조건을 갖는 이동로봇의 궤적추종 예측제어에 유전알고리즘을 적용하여 제어 파라미터 튜닝을 통해 궤적추종오차를 최소화하며, 입력의 제한조건을 반영하기 위해서 quadratic programming을 Hildreth 방법을 적용한다. 컴퓨터 시뮬레이션을 통해 본 논문에서 제안한 방법의 우수성을 기존 방법과 비교하여 확인한다.

• Journal of Power Electronics
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• 제16권6호
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• pp.2016-2023
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• 2016

This paper presents a simple input-constrained current controller for a DC/DC boost converter with stability analysis that considers the nonlinearity of the converter model. The proposed controller is designed to satisfy the inherent input constraints of the converter under a physically reasonable assumption, which is the first contribution of this paper. The second contribution is providing a rigorous proof of the proposed control law, which keeps the closed-loop system along with the internal dynamics stable. The performance of the proposed controller is demonstrated through an experiment employing a 20-kW DC/DC boost converter.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.108.3-108
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• 2001

In this work, we consider a real time optimal control problem of mechanical systems with restrictions for actuators i.e. input restrictions and constraints for the movable area i.e. state constraints. First, we formulate an optimal control problem which evaluates the cost function for a finite time horizon with input restrictions and state constraints of a wheeled vehicle as an example of mechanical systems. In this problem, the differentiability of the cost function is not required and this implies that the problem cannot be solved analytically. Therefore, in this work, we use an optimization method to solve the optimal control problem and a new real time optimization method is proposed to solve the problem. In this method, we provide a parameter that indicates the ...

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.777-780
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• 1995

Accounting for actuator nonlinearities in control loops has often been perceived as an implementation issue and usually excluded in the design of controllers. Nonlinearities treated in this paper are saturation, and they are modelled as an inequality constraint. The CRHPC(Constrained Receding Horizon Predictive Control) with inequality constraints algorithm is used to handle actuator rate and amplitude limits simultaneously or respectively. Optimum values of future control signals are obtained by quadratic programming. Simulated examples show that predictive control law with inequality constraints offers good performance as compared with input clipping.