• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1655-1658
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• 1991

This paper considers the design and analysis of one-step ahead optimal and adaptive controllers, under the restriction that a known constraint on the input amplitude is imposed. It is assumed that the discrete-time single-input, single-output system to be controlled is linear, except for inequality constraints on the input. The objective function to be minimized is an one-step quadratic function, where polynomial weights on the input and output are included. Both the known parameter and unknown parameter (indirect adaptive controller) cases are examined.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.835-841
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• 2014

In this paper we propose a swing-up strategy for a single inverted pendulum. The proposed method has a feature whereby can handle the input and output constraint of a pendulum in a systematic way. For the swing-up of a pendulum, we adopt a 2-DOF control structure that combines the feedforward and feedback control. In order to generate the swing-up feedforward trajectories that satisfy the input and output constraint, we formulate the problem of generating feedforward trajectories as a nonlinear optimal control problem subject to constraints. We illustrate that the proposed method is more flexible than the existing method and provides great freedom in choosing the actuator of the inverted pendulum. Through an experiment, we show that the proposed method can swing a pendulum upward effectively while satisfying all the imposed constraints.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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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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• 1996.10b
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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.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1896-1900
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• 2003

In this paper, a new type of output feedback control, called a receding horizon finite memory control (RHFMC), is proposed for stochastic discrete-time state space systems. Constraints such as linearity and finite memory structure with respect to an input and an output, and unbiasedness from the optimal state feedback control are required in advance. The proposed RHFMC is chosen to minimize an optimal criterion with these constraints. The RHFMC is obtained in an explicit closed form using the output and input information on the recent time interval. It is shown that the RHFMC consists of a receding horizon control and an FIR filter. The stability of the RHFMC is investigated for stochastic systems.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1196-1198
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• 1996

It is well known that the controller output limits have a significant effect on the closed loop system performance. GPC has many tuning-knobs which can he used to minimize actuator activity. Especially, increasing the control weighting $\lambda$ cuts down the controller output variance. Using this property, we propose the GPC with Input constraints(GPCIC) which is based on the relation between control weighting $\lambda$ and optimal solution of the unconstrained GPC. The GPCIC algorithm is the calculation of the optimal $\lambda$ such that the output of the unconstrained GPC is satisfied with the rate Ind the level constraint.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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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.

• Proceedings of the KIEE Conference
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• 2003.11b
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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.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.747-750
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• 1996

This paper considers the problem of robust H$_{\infty}$ control with regional stability constraints via output feedback to assure robust performance for uncertain linear systems. A robust H$_{\infty}$ control problem and the generalized Lyapunov theory are introduced for dealing with the problem, The output feedback H$_{\infty}$ controller makes the controlled outputs settle within a given bound and the control input not to be saturated. The regional stability constraints problem for uncertain systems can be reduced to the problem for the nominal systems by finding sufficient bounds of variations of the closed-loop poles due to modeling uncertainties. A controller design procedure is established using the Lagrange multiplier method. The controller design technique was illustrated on the track-following system of a optical disk drive.ve.

• Steel and Composite Structures
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• v.29 no.1
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• pp.67-75
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• 2018

High performance concrete (HPC) depends on various parameters such as the type of cement, aggregate and water reducer amount. Generally, the ready concrete company in various regions according to the requirements and costs, mix design of concrete as well as type of cement, aggregates, and, amount of other components will vary as a result of moment decisions or dynamic optimization, though the ideal conditions will be more applicable for the design of mix proportion of concrete. This study aimed to apply dynamic optimization for mix design of HPC; consequently, the objective function, decision variables, input and output variables and constraints are defined and also the proposed dynamic optimization model is validated by experimental results. Results indicate that dynamic optimization objective function can be defined in such a way that the compressive strength or performance of all constraints is simultaneously examined, so changing any of the variables at each step of the process input and output data changes the dynamic of the process which makes concrete mix design formidable.