• Journal of Korean Society of Steel Construction
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• v.15 no.6 s.67
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• pp.661-672
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• 2003

The advanced analysis and optimal design of semi-rigid frame were presented. Advanced analysis can predict the combined nonlinear effects of connection, geometry, and material on the behavior and strength of semi-rigid frames. The Kishi-Chen power model was used to describe the nonlinear behavior of semi-rigid connections. Geometric nonlinearity was determined using stability functions. On the other hand, material nonlinearity was determined using the Column Research Council (CRC) tangent modulus and parabolic function. The direct search method proposed by Choi and Kim was used as optimization technique. The member with the largest unit value evaluated using the LRFD interaction equation was replaced one by one with an adjacent larger member selected from the database. The objective function was assumed as the weight of steel frame, with the constraint functions accounting for load-carrying capacities, deflections. inter-story drifts, and ductility requirement. Member sizes determined by the proposed method were compared with those derived using the conventional LRFD method.

• International Journal of Highway Engineering
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• v.5 no.4 s.18
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• pp.13-22
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• 2003

Several design methods from overseas are employed without considering different conditions such as material properties, climate, and traffic condition in this country. Therefore, there are limitations in application. Therefore, new pavement analysis system which is able to design a pavement efficiently and economically should be set up. In this study, 243 probable sections are classified depending on values of layer thickness and elastic modulus, and the effect of load types for the probable sections are analyzed. The section showing larger load distribution is chosen for analysis. As a result of sensitivity, a layer thickness has more influence on pavement than an elastic modulus does. The stress distribution of FWD test load is larger than that of circular load. This study compares outputs between nonlinear elastic model and linear elastic model. Based on the result, this study finds nonlinear elastic model considering stress condition in the ground is recommended for subbase.

• 전자공학회논문지 IE
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• v.46 no.4
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• pp.49-56
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• 2009

In this paper, stabilization inverse optimal control for nonlinear systems with structural uncertainty is considered. 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.33-40
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• 1998

This paper presents an Modified Orthogonal Neural Network(MONN), new modified model of Orthogonal Neural Network(0NN) based on orthogonal functions, and applies it to nonlinear system approximator. ONN proposed by Yang and Tseng, doesn't have the problems of traditional multilayer feedforward neural networks such as the determination of initial weights and the numbers of layers and processing elements. And tranining of ONN converges rapidly. But ONN cannot adapt its orthogonal functions to a given system. The accuracy of ONN, in terms of the minimal possible deviation between system and approximator, is essentially dependent on the choice of basic orthogonal functions. In order to improve ability and effectiveness of approximate nonlinear systems, MONN has an input transformation layer to adapt its basic orthogonal functions to a given nonlinear system. The results show that MONN has the excellent performance of approximate nonlinear systems and the input transfnrmation makes the ability of MONN better than one of ONN.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.21-28
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• 1998

This study develops a variable structure controller using the time-varying nonlinear sliding surface instead of the fixed sliding surface, which has been the robustness against parameter variations and extraneous disturbance during the reaching phase. By appling TS fuzzy algorithm to the regulation of the rionlinear sliding surface, the reaching time of the system trajectory is faster than the fixed method. This proposed scheme has better performance than the conventional method in reaching time, parameter variation and extraneous disturbance. To demonstrate its performance, the proposed control algorithm is applied to a rotational inverted pendulum.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.4
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• pp.329-334
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• 2017

The structures, which are made up with the huge number of degrees-of-freedom and the assembly of substructures, have a great complexity. In order to increase the computational efficiency, the analysis models have to be simplified. Many substructuring techniques have been developed to simplify large-scale engineering problems. The techniques are very powerful for solving nonlinear problems which require many iterative calculations. In this paper, a modal derivatives-based model order reduction method, which is able to capture the stretching-bending coupling behavior in geometrically nonlinear systems, is adopted and investigated for its performance evaluation. The quadratic terms in nonlinear beam theory, such as Green-Lagrange strains, can be explained by the modal derivatives. They can be obtained by taking the modal directional derivatives of eigenmodes and form the second order terms of modal reduction basis. The method proposed is then applied to a co-rotational finite element formulation that is well-suited for geometrically nonlinear problems. Numerical results reveal that the end-shortening effect is very important, in which a conventional modal reduction method does not work unless the full model is used. It is demonstrated that the modal derivative approach yields the best compromised result and is very promising for substructuring large-scale geometrically nonlinear problems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.701-704
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• 2005

PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But They are difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose a nonlinear adaptive PID controller based on a cell-mediated immune response and a gradient descent learning. This algorithm has a simple structure and robustness to system parameters variation. To verify performances of the proposed nonlinear adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.3
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• pp.301-309
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• 2003

The non linear control algorithm with actuator saturation for a randomly excited oscillator has been widely explored and has shown promising results, but the probabilistic analysis of the algorithm has been rarely made due to its non-linear nature and the fact that the analytical solution of probability density function (PDF) for controlled responses does not exist. In this paper, a method for the probabilistic analysis on the non linear control algorithm with actuator saturation is proposed based on the equivalent non linear system method. Numerical examples are given to verify the approximation solution of PDF comparing to a statistically obtained PDF using a Gaussian white noise and a Kanai - Tagimi filtered Gaussian white noise.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.6
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• pp.1-14
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• 2007

Optimal design method of nonlinear damping system for seismic response control of adjacent structures is studied in this paper. The objective functions of the optimal design are defined by structural response and total amount of the dampers. In order to obtain a solution minimizing two mutually conflicting objective functions simultaneously, multi-objective optimization technique based on genetic algorithm is adopted. In addition, stochastic linearization method is embedded into the multi-objective framework to efficiently estimate the seismic responses of the adjacent structures interconnected by nonlinear hysteretic dampers without performing nonlinear time-history analyses. As a numerical example to demonstrate the effectiveness of the proposed technique, 20-story and 10-story buildings are considered and MR dampers of which hysteretic behaviors vary with the magnitude of the input voltage are considered as nonlinear hysteretic damper interconnecting two adjacent buildings. The proposed approach can provide the optimal number and capacities of the MR dampers, which turned out to be more economical than the uniform distribution system while maintaining similar control performance. The proposed damper system is verified to show more stable performance in terms of the pounding probability between two adjacent buildings. The applicability of the proposed method to the design problem for optimally placing semi-active control system is examined as well.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.264-274
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• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.