• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.154-159
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• 1989

This paper presents a motion planning algorithm for conveyor tracking. We formulate the problem as the linear quadratic tracking problem in optimal control theory and solve it through dynamic programming. In the proposed algorithm, the steady-state tracking error is eliminated completely, and the joint torque, velocity, acceleration, and jerks are considered as some constraints. Numerical examples are then presented to demonstrate the utility of the proposed motion planning algorithm.

• 대한수학회보
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• 제57권2호
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• pp.311-330
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• 2020

This paper investigates infinite horizon optimal control problems driven by a class of backward stochastic delay differential equations in Hilbert spaces. We first obtain a prior estimate for the solutions of state equations, by which the existence and uniqueness results are proved. Meanwhile, necessary and sufficient conditions for optimal control problems on an infinite horizon are derived by introducing time-advanced stochastic differential equations as adjoint equations. Finally, the theoretical results are applied to a linear-quadratic control problem.

• 제어로봇시스템학회논문지
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• 제20권11호
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• pp.1118-1124
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• 2014

This paper proposes a novel controller design scheme for multimachine power systems based on the explorized policy iteration. Power systems have several uncertainties on system dynamics due to the various effects of interconnections between generators. To solve this problem, the proposed method solves the LQR (Linear Quadratic Regulation) problem of isolated subsystems without the knowledge of a system matrix and the interconnection parameters of multimachine power systems. By selecting the proper performance indices, it guarantees the stability and convergence of the LQ optimal control. To implement the proposed scheme, the least squares based online method is also investigated in terms of PE (Persistency of Excitation), interconnection parameters and exploration signals. Finally, the performance and effectiveness of the proposed algorithm are demonstrated by numerical simulations of three-machine power systems with governor controllers.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.741-750
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• 2021

The budget deficit is closely related to expansionary fiscal policy as a fiscal instrument to encourage economic growth. This study aims to apply optimal control theory in the Keynesian macroeconomic model for the economy, so that optimal growth can be found. Macroeconomic variables include GDP, consumption, investment, exports, imports, and budget deficit as control variables. This study uses secondary data in the form of time series, the time period 1990 to 2018. Performing optimal control will result in optimal fiscal policy. The optimal determination is done through simulation, for the period 2019-2023. The discrete optimal control problem is to minimize the objective function in the form of a quadratic function against the deviation of the state variable and control variable from the target value and the optimal value. Meanwhile, the constraint is Keynes' macroeconomic model. The results showed that the optimal value of macroeconomic variables has a deviation from the target values consisting of: consumption, investment, exports, imports, GDP, and budget deficit. The largest deviation from the average during the simulation occurs in GDP, followed by investment, exports, and the budget deficit. Meanwhile, the lowest average deviation is found in imports.

• 대한수학회보
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• 제50권1호
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• pp.321-341
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• 2013

In this paper, we discuss the a posteriori error estimates of the semidiscrete mixed finite element methods for quadratic optimal control problems governed by linear hyperbolic equations. The state and the co-state are discretized by the order $k$ Raviart-Thomas mixed finite element spaces and the control is approximated by piecewise polynomials of order $k(k{\geq}0)$. Using mixed elliptic reconstruction method, a posterior $L^{\infty}(L^2)$-error estimates for both the state and the control approximation are derived. Such estimates, which are apparently not available in the literature, are an important step towards developing reliable adaptive mixed finite element approximation schemes for the control problem.

• 한국인터넷방송통신학회논문지
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• 제8권2호
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• pp.49-55
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• 2008

To address the convergence issue of power control algorithms, a number of algorithms have been developed hat shape the dynamics of up-link power control for cellular network. Power algorithms based on fixed point iterations can be accelerated by the use of various methods, one of the simplest being the use of Newton iterations, however, this method has the disadvantage which not only needs derivatives of the cost function but also may be weak to noisy environment. we showed performance of the power control schemes to solve the fixed point problem under static or stationary channel. They proved goof performance to solve the fixed point problem due to their predictor based optimal control and quadratic convergence rate. Here, we apply the proposed power control schemes to the problem of the dynamic channel or to dynamic time varying link gains. The rigorous simulation results demonstrated the validity of our approach.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.276-278
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• 1992

This paper presents a method of hierachical optimal control for time invariant large scale systems via Single Term Walsh Series. It is well known that the optimal control of a large scale system with quadratic performance criteria often involves the determination of time varying feedback gain matrix by solving the matrix Riccati differential equation, which is usually quite difficult. Therefore, in order to solve the problem, this paper is introduced to Single Term Walsh Series. The advantages of proposed method are simple and attractive for the control of large scale system in computation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 D
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• pp.841-843
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• 1997

In this paper we present a Genetic approach to select weighting matrices of LQ(Linear Quadratic) controller for optimal TCSC(Thyristor Controlled Series Capacitor) control. A design of LQ controller depends on choosing weighting matrices. The selection of weighting matrices is usually carried out by trial and error, which is not a trivial problem. We proposed a efficient method using GA of finding weighting matrices for optimal control law. The proposed GA method was applied to design LQ controller of TCSC in one machine infinite bus system and showed good results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 A
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• pp.226-228
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• 2002

This paper presents a new control approach for designing a coordinated controller for static VAR compensator system. A SVC constructed by a Fixed Capacitor and a Thyristor Controlled Reactor is designed and implemented to improve the damping of a synchronous generator, as well as controlling the system voltage. A design of linear quadratic controller based on optimal controller depends on choosing weighting matrices. A coordinated optimal controller is achieved by minimizing a quadratic performance index using dynamic programming techniques. The selection of weighting matrices is usually carried out by trial and error which is not a trivial problem. We proposed a efficient method using GA of finding weighting matrices for optimal control law. Thus, we prove the usefulness of proposed method to improve the stability of single machine-infinite bus with SVC system.

• 품질경영학회지
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• 제32권3호
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• pp.109-125
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• 2004

This paper deals with an optimal process control problem in production of hull structural steel plate with high defective rate. The main quality characteristic(dependent variable) is the internal quality(defect) of plates and is dependent on process parameters(independent variables). The dependent variable(quality characteristics) has three categorical ordinal data and there are 35 independent variables(29 continuous variables and 6 categorical variables). In this paper, we determine the main factors and to develop the mathematical model between internal quality predicted probabilities and the main factors. Secondly, we find out the optimal process condition of main factors through analysis of variance(ANOVA) using simulation. We consider three models to obtain the main factors and the optimal process condition: linear, quadratic, error models.