• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.280-284
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• 1998

Many classes of nonlinear systems can be represented by Volterra kernel expansion. Therefore, identification of Volterra kernels of nonlinear system is an important task for obtaining the nonlinear characteristics of the nonlinear system. Although one of the authors has recently proposed a new method for obtaining the Volterra kernels of a nonlinear system by use of M-sequence and correlation technique, our mettled of nonlinear system identification is limited to Wiener-type nonlinear system and we can not apply this method to the identification of Hammerstein-type nonlinear system. This paper describes a new mettled for obtaining Volterra kernels of Hammerstein nonlinear system by adding a linear element in front of tile Hammerstein system. First we calculate the linear element of Hammerstein system by use of conventional correlation method. Secondly, we put a linear element in front of Hammerstein system. Then the total system becomes Wiener-type nonlinear system. Therefore we can use our method on Volterra kernel identification by use of M-sequence. Thus we get the coefficients of the approximation polynomial of nonlinear element of Hammerstein system. From the results of simulation, a good agreement with theoretical considerations is obtained, showing a wide applicability of our method.

• Journal of Ocean Engineering and Technology
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• v.10 no.1
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• pp.17-24
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• 1996

In the present paper nonlinear normal modes of a rotor system is studied. The methodology to obtain the nonlinear normal modes is based on center manifold reduction technique. It also provides a way of nonlinear coordinate transform from the physical cordinates to the modal coordinates and an idea of individual nonlinear modal dynamics. In order to apply the present method to a rotor dynamics a single mass rotor system on nonlinear elastic supports is employed and the nonlinear normal modes of the system are obtained.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.327-332
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• 2003

A new discretization method for nonlinear system with time-delay is proposed. It is based on the well-known Taylor series expansion and the zero-order hold (ZOH) assumption. We know that a discretization of linear system can be obtained with the ZOH assumption and within the sampling interval. A similar line of thinking is available in nonlinear case. The mathematical structure of the new discretization method is explored and under the structure, the sampled-data representation of nonlinear system including time-delay is computed. Provided that the discrete form of the single input nonlinear system with time-delay is derived, this result is easily extended to nonlinear system with multi-input time-delay. For simplicity two inputs are considered in this study. It is enough to generalize that of multiple inputs. Finally, the time-discretization of non-affine nonlinear system with time-delay is investigated for apply all nonlinear system

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.11
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• pp.760-767
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• 2004

In this paper, we propose sufficient conditions under which nonlinear systems can be transformed into nonlinear observer canonical form in the extended state space by virtue of dynamic system extension. The proposed scheme weakens two major restrictions of observer error linearization technique. Once a nonlinear system is transformed into nonlinear observer canonical form using dynamic system extension, a state observer can be easily designed. Two illustrative examples are included in order to compare the proposed scheme and observer error linearization method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.7
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• pp.1055-1063
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• 2004

In this study, we developed an automatic veneer sorting system controlled by nonlinear friction and nonlinear stiffness. With these nonlinear characteristics, it was difficult to analysis and to control the system in the fast. However it is necessary to consider nonlinear characteristics to satisfy accurate and rapid control demand in these days. We used not only nonlinear friction but also nonlinear stiffness and combined both to control the system. An experimental device was designed with 4 AC servo-motors and 2 Sensors. Through a series of experiment, we found nonlinear friction characteristics among roller versus veneer and veneer versus veneer and nonlinear stiffness characteristics with stacked veneers. Finally, we showed that the proposed control algorithm was very effective for veneer sorting system with nonlinear friction and stiffness.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.158-164
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• 2003

The suspension system of cars is composed of dampers and springs, which usually have nonlinear characteristics. The nonlinear characteristics make the differences in the results of analytical models and experiments. In this study, the nonlinear system identification method which does not assume a special form for nonlinear dynamic systems and minimize the error by calculating the error reduction ratio is devised to estimate the nonlinear parameters of the suspension system of an EF-SONATA car from the field running test data. The results show that the spring has a cubic nonlinear term and the damper has a coupled nonlinear term. Also, the numerical results with the estimated nonlinear parameters agree well with the field test data for the different running speeds.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.597-600
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• 1997

In this paper, the authors propose a new method for linearizing a nonlinear dynamical system by use of polynomial compensation. In this method, an M-sequence is applied to the nonlinear system and the crosscorrelation function between the input and the output gives us every crosssections of Volterra kernels of the nonlinear system up to 3rd order. We construct a polynomial compensation function from comparison between lst order Volterra kernel and high order kernels. The polynomial compensation function is, in this case, of third order whose coefficients are variable depending on the amplitude of the input signal. Once we can get compensation function of nonlinear system, we can construct a linearization scheme of the nonlinear system. That is. the effect of second and third order Volterra kernels are subtracted from the output, thus we obtain a sort of linearized output. The authors applied this method to a saturation-type nonlinear system by simulation, and the results show good agreement with the theoretical considerations.

• Journal of Korean Society for Quality Management
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• v.28 no.1
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• pp.95-104
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• 2000

There exist many computer algorithms to solve a system of nonlinear equations. But in case nonlinear equations are complex it is not easy to solve a system of nonlinear equations. In this paper we consider the method of using NLIN procedure in SAS/STAT to solve a system of nonlinear equations.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.592-597
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• 2003

In this paper, some differential geometric conditions for the observer using a recurrent neural network are provided in terms of a stochastic nonlinear system control. In the stochastic nonlinear system, it is necessary to make an additional condition for observation of stochastic nonlinear system, called perfect filtering condition. In addition, we provide a observer using a recurrent neural network for the observation of a stochastic nonlinear system with the proposed observation conditions. Computer simulation shows that the control performance of the stochastic nonlinear system with a observer using a recurrent neural network satisfying the proposed conditions is more efficient than the conventional observer as Kalman filter

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.18-21
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• 2001

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.