• Structural Engineering and Mechanics
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• v.45 no.4
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• pp.521-542
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• 2013

In this study a truss model is used for the geometrically nonlinear static and dynamic analysis of a thin shallow arch subject to snap-through. Thanks to the very simple geometry of a truss, the equilibrium conditions can be easily written and the global stiffness matrix can be easily updated with respect to the deformed structure, within each step of the analysis. A very coarse discretization is applied; so, in a very simple way, the high frequency modes are suppressed from the beginning and there is no need to develop a complicated reduced-order technique. Two short computer programs have been developed for the geometrically nonlinear static analysis by displacement control of a plane truss model of a structure as well as for its dynamic analysis by the step-by-step time integration algorithm of trapezoidal rule, combined with a predictor-corrector technique. These two short, fully documented computer programs are applied on the geometrically nonlinear static and dynamic analysis of a specific thin shallow arch subject to snap-through.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1197-1200
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• 2010

The problem of designing a nonlinear sliding surface for an uncertain system is considered. The proposed sliding surface comprises a linear time invariant term and an additional time varying nonlinear term. It is assumed that a linear sliding surface parameter matrix guaranteeing the asymptotic stability of the sliding mode dynamics is given. The linear sliding surface parameter matrix is used for the linear term of the proposed sliding surface. The additional nonlinear term is designed so that a Lyapunov function decreases more rapidly. By including the additional nonlinear term to the linear sliding surface parameter matrix we obtain a nonlinear sliding surface such that the speed of responses is improved. We also give a switching feedback control law inducing a stable sliding motion in finite time. Finally, we give an LMI-based design algorithm, together with a design example.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.89-91
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• 2007

In this paper, a new approach for a sampled-data representation of nonlinear system that has time-delayed multi-input is proposed. That is largely devoid of illconditioning and is suitable for any nonlinear problem. The new scheme is applied to nonlinear systems with two or three inputs; and then the delayed multi-input general equation is derived. The method is based on thematrix exponential theory. Itdoes not require excessive computational resources and lends itself to a short and robust piece of software that can be easily inserted into large simulation packages. A performance of the proposed method is evaluated using a nonlinear system with time-delay: maneuvering an automobile.

• Structural Engineering and Mechanics
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• v.51 no.3
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• pp.519-530
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• 2014

This paper attempts to investigate the nonlinear dynamic analysis of strong nonlinear problems by proposing a new analytical method called Hamiltonian Approach (HA). Two different cases are studied to show the accuracy and efficiency of the method. This approach prepares us to obtain the nonlinear frequency of the nonlinear systems with the first order of the solution with a high accuracy. Finally, to verify the results we present some comparisons between the results of Hamiltonian approach and numerical solutions using Runge-Kutta's [RK] algorithm. This approach has a powerful concept and the high accuracy, so it can be apply to any conservative nonlinear problems without any limitations.

• Structural Engineering and Mechanics
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• v.26 no.3
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• pp.251-262
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• 2007

This paper examines the application of artificial neural networks (ANN) to the response prediction of geometrically nonlinear truss structures. Two types of analysis (deterministic and probabilistic analyses) are considered. A three-layer feed-forward backpropagation network with three input nodes, five hidden layer nodes and two output nodes is firstly developed for the deterministic response analysis. Then a back propagation training algorithm with Bayesian regularization is used to train the network. The trained network is then successfully combined with a direct Monte Carlo Simulation (MCS) to perform a probabilistic response analysis of geometrically nonlinear truss structures. Finally, the proposed ANN is applied to predict the response of a geometrically nonlinear truss structure. It is found that the proposed ANN is very efficient and reasonable in predicting the response of geometrically nonlinear truss structures.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.346-352
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• 1998

In this paper, we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastic approximation method, it is regarded as a stochastic recursive filter algorithm. In addition, we provide a filtering and control condition for a stochastic nonlinear system, called perfect filtering condition, in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence, the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable. and the proposed neural controller is more efficient than the conventional LQG controller and the canoni al LQ-Neural controller.

• Journal of Mechanical Science and Technology
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• v.18 no.7
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• pp.1107-1120
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• 2004

This study proposes a new scheme for the sampled-data representation of nonlinear systems with time-delayed multi-input. The proposed scheme is based on the Taylor-series expansion and zero-order hold assumption. The mathematical structure of a new discretization scheme is explored. On the basis of this structure, the sampled-data representation of nonlinear systems including time-delay is derived. The new scheme is applied to nonlinear systems with two inputs and then the delayed multi-input general equation is derived. The resulting time-discretization provides a finite-dimensional representation of nonlinear control systems with time-delay enabling existing controller design techniques to be applied to them. In order to evaluate the tracking performance of the proposed scheme, an algorithm is tested for some of the examples including maneuvering of an automobile and a 2-DOF mechanical system.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1262-1266
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• 2003

Fuzzy sliding mode controller for a class of uncertain nonlinear dynamical systems is proposed and analyzed. The controller's construction and its analysis involve sliding modes. The proposed controller consists of two components. Sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum. The results show that both alleviation of chattering and performance are achieved.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.133-136
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• 2003

This paper presents a fuzzy control scheme for backing up control of Truck-Trailer, which is nonlinear and unstable by using TSK(Takagi-Sugeno-kang) fuzzy system. The nonlinear system of Truck-Trailer was expressed by using TSK fuzzy model, and the TSK fuzzy controller was designed from TSK fuzzy model. The usefulness of the proposed algorithm for backing up truck-trailer is certificated by the computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.490-493
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• 1996

This paper presents a control scheme for the motion of a 2 DOF robot manipulator. Robot manipulators are multivariable nonlinear systems. Fuzzy logic is avaliable human-like control without complex mathematical operation and is suitable to nonlinear system control. In this paper, Implementation of fuzzy logic control of robotic manipulators shows. Algorithm has been performed with simulation packages MATRIXx and SystemBuild.