• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.236-236
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• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.

• Bulletin of the Korean Chemical Society
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• v.24 no.8
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• pp.1107-1110
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• 2003

Valuable insight into the nonlinear dynamics of a system can be gleaned from its response to a single intense short pulse. We derive expressions for the corresponding nonlinear response functions and show that the fluctuation-dissipation theorem may be extended beyond the linear response limit to an arbitrary pulse intensity. As an illustrative example, we calculate response functions up to 11th order for the regular Lorentz gas in two dimensions.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.563-572
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• 2011

In this paper, the Lie symmetry analysis is performed on the three mixed second-order PDEs, which arise in fluid dynamics, nonlinear wave theory and plasma physics, etc. The symmetries and similarity reductions of the equations are obtained, and the exact solutions to the equations are investigated by the dynamical system and power series methods. Then, the exact solutions to the general types of PDEs are considered through a variable transformation. At last, the symmetry and integration method is employed for reducing the nonlinear ODEs.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.191-194
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• 2001

The ozone forecasting systems have many problems because the mechanism of the ozone concentration is highly complex, nonlinear, and nonstationary. Also, the results of prediction are not a good performance so far, especially in the high-level ozone concentration. This paper describes the modeling method of the ozone prediction system using neuro-fuzzy approaches and fuzzy clustering. The dynamic polynomial neural network (DPNN) based upon a typical algorithm of GMDH (group method of data handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.1
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• pp.42-47
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• 2018

This paper describes the dynamical behavior of the bicycle and its nonlinear effect when magnetic repulsive forces are applied to the bicycle cushion system. A finite-element method was used to obtain its reliabilities by comparing the experimental and numerical values and select the proper magnet sizes. The Equivalent spring stiffness values were evaluated in terms of both linear and nonlinear approximations, where the nonlinear effect was specifically investigated for the ride comfort. The corresponding equations of linear and nonlinear motion were derived for the numerical model with three degrees of freedom. Dynamic behaviors were observed when the bicycle ran over a curvilinear road in the form of a sinusoidal curve. The analysis in this paper for the observed nonlinearity of magnetic repulsive forces will be a useful guide to more accurately predict the cushion design for any vehicle 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.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.2
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• pp.33-39
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• 1996

This paper presents the linearized fuzzy modeling technique of nonlinear dynamical system and the stability analysis of fuzzy control system. Firstly, the nonlinear system is partitionized by multiple linear fuzzy subcontrol systems based on fuzzy linguistic variables and fuzzy rules. Secondly, the disturbance adaptaion controllers which guarantee the global asymptotic stability of each fuzzy subsystem by an optimal feedback control law are designed and the stability analysis procedures of the total fuzzy control system using Lyapunov functions and eigenvalues are discussed in detail through a given illustrative example.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.740-743
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• 1998

This paper presents an optimization algorithm for a stable Dynamic Neural Network (DNN) using genetic algorithm. Optimized DNN is applied to a problem of controlling nonlinear dynamical systems. DNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDNN has considerably fewer weights than DNN. The object of proposed algorithm is to the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling, a nonlinear dynamic system using the proposed optimized SDNN considering stability' is demonstrated by case studies.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.141-144
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• 1999

The authors have recently developed a new method for identification of Volterra kernels of nonlinear systems by use of pseudorandom M-sequence and correlation technique. And it is shown that nonlinear systems which can be expressed by Volterra series expansion are well identified by use of this method. However, there exist many nonlinear systems which can not be expressed by Volterra series mathematically. A nonlinear system having backlash type nonliear element is one of those systems, since backlash type nonlinear element has multi-valued function between its input and output. Since Volterra kernel expression of nonlinear system is one of the most useful representations of non-linear dynamical systems, it is of interest how the method of Volterra kernel identification can be ar plied to such backlash type nonlinear system. The authors have investigated the effect of application of Volterra kernel identification to those non-linear systems which, accurately speaking, is difficult to express by use of Volterra kernel expression. A pseudorandom M-sequence is applied to a nonlinear backlash-type system, and the crosscorrelation function is measured and Volterra kernels are obtained. The comparison of actual output and the estimated output by use of measured Volterra kernels show that we can still use Volterra kernel representation for those backlash-type nonlinear systems.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.14 no.3
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• pp.163-173
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• 2010

In this work, we consider a nonlinear budworm model by a system of three ordinary differential equations originally created by Ludwig et al. in 1978. The nonlinear system describes the dynamics of the interaction between a budworm and a fir forest. We introduce stability techniques to analyze the dynamical behavior of this nonlinear system. Then we use constant effort harvesting techniques to control the budworm population. We also give numerical simulations of the population model with harvest and without harvest.