• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.969-973
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• 1990

This paper presents a method of suboptimal control for nonlinear systems via block pulse transformation. The adaptive optimal control scheme proposed by J.P. Matuszewski is introduced to minimize the performance index. Nonlinear systems are controlled using the obtained optimal control via block pulse transformation. The proposed method is simple and computationally advantageous. Viablity of the this method is established with simulation results for the van der Pol equation for comparision with other methods.

• Journal of Korean Institute of Industrial Engineers
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• v.4 no.2
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• pp.107-118
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• 1978

The paper illustrates a possible application of control theory to an economic growth system. Simultaneous nonlinear system of differential equations has been modeled which is different from the traditional formulation, based on the theory of economic growth for a two-sector (dual) economy. Necessary and sufficient conditions for the existence of the optimal control are derived directly from the Hamiltonian, and the optimal controls are also obtained by solving simultaneous equations. Obtaining the trajectories of the optimal control and state variables, however, should rely on the numerical procedures. Empirical application has been conducted for the case of the Korean economy as an illustration.

• Nonlinear Functional Analysis and Applications
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• v.28 no.3
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• pp.659-670
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• 2023

In this paper, we study the distributed optimal control problem of a coupled system of the host-pathogen model. The system consists of the density of the susceptible host, the density of the infected host, and the density of pathogen particles. Our main goal is to minimize the infected density and also to decrease the cost of the drugs administered. First, we prove the existence and uniqueness of solutions for the proposed problem. Then, the existence of the optimal control is established and necessary optimality conditions are also derived.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.71.2-71
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• 2001

This paper deals with nonlinear system modelling using neural network and genetic algorithm. Application of neural network to control and identification is actively studied because of their approximating ability of nonlinear function. It is important to design the neural network with optimal structure for minimum error and fast response time. Genetic algorithm is getting more popular nowadays because of their simplicity and robustness. In this paper, We optimize neural network structure using genetic algorithm. The genetic algorithm uses binary coding for neural network structure and search for optimal neural network structure of minimum error and response time. Through extensive simulation, Optimal neural network structure is shown to be effective for ...

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.8
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• pp.792-804
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• 1990

This paper describes an efficient optimization algorithm by calculating sensitivity function for power system stabilization. In power system, the dynamic performance of exciter, governor etc. following a disturbance can be presented by a nonlinear differential equation. Since a nonlinear equation can be linearized for small disturbances, the state equation is expressed by a system matrix with system parameters. The objective function for power system operation will be related to the system parameter and the initial state at the optimal control condition for control or stabilization. The object function sensitivity to the system parameter can be considered to be effective in selecting the optimal parameter of the system.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.57-68
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• 1995

A general procedure for the numerical solution of coupled, nonlinear, differential two-point boundary-value problems, solutions of which are crucial to the controller design, has been developed and demonstrated. A fixed-end-points, free-terminal-time, optimal-control problem, which is derived from Pontryagin's Maximum Principle, is solved by an extension of Davidenko's method, a differential form of Newton's method, for algebraic root finding. By a discretization process like finite differences, the differential equations are converted to a nonlinear algebraic system. Davidenko's method reconverts this into a pseudo-time-dependent set of implicitly coupled ODEs suitable for solution by modern, high-performance solvers. Another important advantage of Davidenko's method related to the time-optimal problem is that the terminal time can be computed by treating this unkown as an additional variable and sup- plying the Hamiltonian at the terminal time as an additional equation. Davidenko's method uas used to produce optimal trajectories of a single-degree-of-freedom problem. This numerical method provides switching times for open-loop control, minimized terminal time and optimal input torque sequences. This numerical technique could easily be adapted to the multi-point boundary-value problems.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.5
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• pp.173-178
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• 1985

The control of a linear system with random coefficients is discussed here. The cost function is of a quadratic form and the random coefficients are assumed to be completely observable by the controller. Stochastic Process involved in the problem by the controller. Stochastic Process involved in the problem formulation is presented to be the unique strong solution to the corresponding stochastic differential equations. Condition for the optimal control is represented through the existence of solution to a Cauchy problem for the given nonlinear partial differential equation. The optimal control is shown to be a linear function of the states and a nonlinear function of random parameters.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.1002-1004
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• 1999

The aim of this paper is to suggest a design method of the optimal multivariable $H_{\infty}$ control system using genetic algorithm(GA). This $H_{\infty}$ control system is designed by applying GA to the optimal determination of weighting functions and design parameter ${\gamma}$ that are given by Glover-Doyle algorithm which can design $H_{\infty}$ controller in the state space. The effectiveness of this $H_{\infty}$ control system is verified by nonlinear simulation.

• Journal of the Korea Society of Computer and Information
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• v.1 no.1
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• pp.159-170
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• 1996

In this paper, we propose a generating method for the optimal rules for the nonlinear control system using evolution algorithms and fuzzy controller. With the aid of evolution algorithms optimal rules of fuzzy logic system can be automatic designed without human expert's priori experience and. knowledge. and ran be intelligent control. The approachpresented here generating rules by self-tuning the parameters of membership functions and searchs the optimal control rules based on a fitness value which Is tile defined performance criterion. Computer simulations demonstrates the usefulness of the proposed method In non -linear systems.

• Kyungpook Mathematical Journal
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• v.51 no.4
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• pp.353-364
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• 2011

Duality in the optimal harvesting for a nonlinear age-spatial structured population dynamic model is studied in the framework of optimal control problem. In this paper the duality theory that displays the conjugacy of the primal problem is established and an application is given. Duality theory plays an important role in both optimization theory and methodology and the results may be applied to a realistic biological system on the point of optimal harvesting.