• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.227-233
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• 1996

The Inverted Pendulum has been one of most popular nonlinear dynamic systems for the exploration of control techniques. This paper presents a new linear optimal control techniques and nonlinear neural network learning methods. The multiayered neural networks are used to add nonlinear effects on the linear optimal regulator(LQR). The new regulator can compensate nonlinear system uncertainties that are not considered in the LQR design, and can tolerated a wider range of uncertainties than the LQR alone. The new regulator has two neural networks for modeling and control. The neural network for modeling is used to obtain a more accurate model than the given mathematical equations. The neural network for control is used to overcome deficiencies by adding corrections to the linear coefficients of the LQR and by adding nonlinear effects on the LQR. Computer simulations are performed to show the applicability and a more robust regulator than the LQR alone.

• Earthquakes and Structures
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• v.22 no.1
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• pp.95-107
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• 2022

This paper introduces a novel, rigorous, and efficient probabilistic methodology for the performance-based optimal design (PBOD) of semi-active tuned mass damper (SATMD) for seismically excited nonlinear structures. The proposed methodology is consistent with the modern performance-based earthquake engineering framework and aims to design reliable control systems. To this end, an optimization problem has been defined which considers the parameters of control systems as design variables and minimization of the probability of exceeding a targeted structural performance level during the lifetime as an objective function with a constraint on the failure probability of stroke length damage state associated with mass damper mechanism. The effectiveness of the proposed methodology is illustrated through a numerical example of performance analysis of an eight-story nonlinear shear building frame with hysteretic bilinear behavior. The SATMD with variable stiffness and damping have been designed separately with different mass ratios. Their performance has been compared with that of uncontrolled structure and the structure controlled with passive TMD in terms of probabilistic demand curves, response hazard curves, fragility curves, and exceedance probability of performance levels during the lifetime. Numerical results show the effectiveness, simplicity, and reliability of the proposed PBOD method in designing SATMD with variable stiffness and damping for the nonlinear frames where they have reduced the exceedance probability of the structure up to 49% and 44%, respectively.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.259-267
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• 2003

In this paper, we propose a nonlinear variable PID controller using a cell-mediated immune response. An immune feedback response is based on the functioning of biological T-cells. An immune feedback response and P-controller of conventional PID controllers resemble each other in role and mechanism. Therefore, we extend immune feedback mechanism to nonlinear PE controller. And in order to choose the optimal nonlinear PID controller games, we also propose the on-line tuning algorithm of nonlinear functions parameters in immune feedback mechanism. The trained parameters of nonlinear functions are adapted to the variations of the system parameters and any command velocity. And the adapted parameters obtained outputs of nonlinear functions with an optimal control performance. To verify performances of the proposed control systems, the speed control of nonlinear BC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system variations.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.206-209
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• 1988

For feedback control of a linear dynamic system the optimum linear slace regulator (OLSR) can be implemented only if all state are available for feedback. This work demonstrates that when only the output state is available for feedback, a nonlinear controllers can be improved performance over that obtained by a proportional controller. This paper found the optimal control law by well-known dynamic programming and principles of optimality. Thus, performance of both proportional and nonlinear controllers is compared with performance of optimum linear state regulator.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2651-2659
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• 2009

In this paper, inverse optimal control for nonlinear systems with structural uncertainty is considered. The first, the bounded of structural uncertainty is introduced and based on the control Lyapunov function, a theorem for the globally asymptotic stability is presented. From this a less conservative condition for the inverse optimal control is derived. The result is used to design an inverse optimal controller for a class of nonlinear systems, that improves and extends the existing results. The class of nonlinear system considered is also enlarger. The simulation results show the effectiveness of the method.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.51-53
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• 1993

This paper proposes a design of the tracking controller using artificial neural network and the compare the result with a result of optimal controller. In practical use, conventional Optimal controller has some limits. First, optimal controller can be designed only for linear system. Second, for many systems state observation is difficult or sometimes impossible. But the controller using artificial neural network does not need mathmatical model of the system including state observation, so it can be used for both linear and nonlinear system with no additional cost for nonlinearity. Designed multi layer neural network controller is composed of two parts, feedforward controller gives a steady state input & feedback controller gives transient input via minimizing the quadratic cost function. From the comparison of the results of the simulation of linear & nonlinear plant, the plant controlled by using neural network controller shows the trajectory similar to that of the plant controlled by an optimal controller.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.1
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• pp.301-309
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• 1995

Shape optimal design of an excavator boom to minimize weight can be formulated as a nonlinear programming problem with an automesh refinement carried out by using the finite element method. The design variables are the radii and the coordinates of the circle to describe the excavator boundary shape. In addition to the displacement and stress constraints, geometric constraints are imposed such that the nodes cannot cross the certain range. The optimum design is obtained by using the PLBA nonlinear programming code. The sensitivity derivatives are calculated using the semi-analytical scheme. Numerical results of an excavator boom show potential for weight reduction of 4.4%(65.6 kgf) when considering the displacement, stress and geometric constraints.

• 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.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.344-351
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• 2003

In order to consider the characteristics of nonlinear dynamic responses of seismic isolated bridges reasonably, piers and isolators are modeled as a 2-DOF bilinear system. Then nonlinear time-history earthquake response analysis is accomplished many artificial input ground motions which were generated to reflect the characteristics of earthquakes. Damage probabilities and failure probabilities of each structural elements of the brides are calculated by using Monte-Carlo simulation method. Based on LCC evaluation considering various cost items of direct/indirect damage costs, the optimal design method of seismic isolated bridges is proposed. By using a sensitivity analysis about the design variables and a cost effectiveness evaluation in the viewpoint of LCC, the validity and the adequacy of proposed optimal design method are verified.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.369-372
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• 2006

Optimal engineering design is challenging because nonlinear objective functions need to be evaluated in a high-dimensional space. This paper presents a data-mining aided optimal design method. The method is employed in designing an optimal multi-station fixture layout. Its benefit is demonstrated by a comparison with currently available optimization methods.