• Journal of the Korea Institute of Military Science and Technology
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• v.1 no.1
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• pp.211-218
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• 1998

As a part of trajectory modulation to increase system survivability and terminal effectiveness, impact angle control is required in the terminal phase of tactical missile systems. The missile systems are not allowed to have high altitude to reduce probability of detection by sensors of missile defense systems. In this paper, an analytic form of a time-optimal control law is suggested in the case of constrained missile maneuverability and impact angle under the assumption of a zero-lag autopilot. The control law is obtained by establishing optimal missile-target engagement geometry in the vertical plane. Extension of the law for missiles with autopilot response lags requiring a numerical solution is studied by introducing an iterative algorithm for optimal switching time determination of which the initial switching instants are obtained from the analytic solution. Also suggested is a closed-form impact angle control law derived by an energy-optimal approach. The performances of the proposed guidance laws are evaluated by a series of computer runs.

• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.203-214
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• 2005

In this study the optimal control of fed-batch glycerol fermentation is investigated based on an impulsive dynamical system. Considering the sudden increase of the glycerol and alkali in fed-batch culture of biodissimilation of glycerol to 1,3-propanediol, this paper proposes a non-linear impulsive system of fed-batch culture. The existence, uniqueness and regularity properties of piecewise solution for the system are proved. In view of the controllability of volumes of glycerol added to the reactor instantaneously, the paper constructs an optimal control model based on the nonlinear impulsive system and the existence of the optimal control is obtained. The control variables here are the moments and the sizes of jumps in the states at the discrete instants and the objective is to maximize the productivity of 1,3-propanediol over one cycle.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.10
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• pp.1075-1085
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• 1990

This paper presents a new method for the optimal control of the distributed parameter systems by a decentralized computational procedure. Approximate lumped parameter models are derived by using the Galerkin method employing the Legendre polynomials as the basis functions. The distributed parameter systems, however, are transformed into the large scale lumped parameter models. And thus, the decentralized control scheme is introduced to determine the optimal control inputs for the obtained lumped parameter models. In addition, an approach to block pulse functions is applied to solve the optimal control problems of the obtained lumped parameter models. The proposed method is simple and efficient in computation for the optimal control of distributed paramter systems. Illustrative examples given to demonstrate the validity of the presently proposed method.

• Journal of Power System Engineering
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• v.8 no.1
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• pp.69-74
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• 2004

A structure-control combined optimal design problem is discussed taking a 3-D truss structure as a design object. We use descriptor forms for a controlled object and a generalized plant because the structural parameters appear naturally in these forms. We consider not only minimum weight design problem for structure system, but also suppression problem of the effect of disturbances for control system as the purpose of the design. A numerical example shows the validity of combined optimal design of structure and control systems. We also consider the validity of sensor-actuator collocation for control system design in this paper.

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.46-51
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• 2007

The near-optimal control algorithm for central cooling system has been developed for minimizing energy consumption while maintaining the comfort of indoor thermal environment in terms of the environmental variables such as time varying indoor cooling load and outdoor temperatures. The optimal set-points of control parameters with near-optimal control are supply air temperature and chilled water temperature. This study has been done by using LapVIEW program with PID control in order to analyze the central cooling system energy saving.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.455-458
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• 2003

An optimal control approach to robust control design is proposed in this study for rigid robotic systems under the unknown load and the other uncertainties. The uncertainties are quadratically bounded for some positive definite matrix. Iterative method to find the matrix is shown. Simulations arc made for a weight-lifting operation of a two-link manipulator and the robust control performance of robotic systems by the proposed algorithm is remarkable.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.12
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• pp.176-182
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• 2003

An optimal control approach to robust control design is proposed in this study for rigid robotic systems under the unknown load and the other uncertainties. The uncertainties are quadratically bounded for some positive definite matrix. Iterative method to find the matrix is shown. Simulations are made for a weight-lifting operation of a two-link manipulator and the robust control performance of robotic systems by the proposed algorithm is remarkable.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.698-706
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• 2016

In order to move mobile robots to desired locations in a minimum time, optimal control problems have to be solved; however, their analytic solutions are almost impossible to obtain due to robot nonlinear equations. This paper presents a method to get optimal control gains of mobile robots using genetic algorithms. Since the optimal control gains of mobile robots depend on the initial conditions, the initial condition range is discretized to form some grid points, and genetic algorithms are applied to provide the optimal control gains for the corresponding grid points. The optimal control gains for general initial conditions may be obtained by use of neural networks. So the optimal control gains and the corresponding grid points are used to train neural networks. The trained neural networks can supply pseudo-optimal control gains. Finally simulation studies have been conducted to verify the effectiveness of the method presented in this paper.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.282-288
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• 2003

It is known that HIV (Human Immunodeficiency Virus) infection, which causes AIDS after some latent period, is a dynamic process that can be modeled mathematically. Effects of available anti-viral drugs, which prevent HIV from infecting healthy cells, can also be included in the model. In this paper we illustrate control theory can be applied to a model of HIV infection. In particular, the drug dose is regarded as control input and the goal is to excite an immune response so that the symptom of infected patient should not be developed into AIDS. Finite horizon optimal control is employed to obtain the optimal schedule of drug dose since the model is highly nonlinear and we want maximum performance for enhancing the immune response. From the simulation studies, we found that gradual reduction of drug dose is important for the optimality. We also demonstrate the obtained open-loop optimal control is vulnerable to parameter variation of the model and measurement noise. To overcome this difficulty, we finally present nonlinear receding horizon control to incorporate feedback in the drug treatment.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1784_1785
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• 2009

This paper presents sliding mode control and optimal control techniques for controlling the speed of permanent magnet synchronous motor. Virtual sliding surface has nominal dynamics of an original system. The performance of the system with sliding mode control and optimal control is compared with the response of the nominal system. As a result, the sliding mode control and optimal control has robustness against the system uncertainties.