• Journal of Advanced Marine Engineering and Technology
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• v.41 no.1
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• pp.105-110
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• 2017

This paper introduces a heave compensation system for offshore crane when it subjected to unexpected disturbances such as ocean waves, tidal currents or winds and their external force. The dynamic model consists of a crane which is considered to behave in the same manner as a rigid body, a hydraulic driven winch, an elastic rope and a payload. To keep the payload from moving upwards and downwards, PD(Proportional-Derivative) control was applied by using linearization. In order to achieve a better performance, the sliding mode control and the nonlinear generalized predictive control algorithm was applied according to the time-delay. As a result, the oscillating amplitude of the payload was reduced by the control algorithm. Considering the time-delay involved in the system to be one second, nonlinear generalized predictive controller with a robust controller was a suitable control algorithm for this heave compensation system because it made the position of te payload reach the desired position with the minimum error. This paper presented a control algorithm using the robust control and its simulation results.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.73-79
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• 2009

For the shake of control for movement object, control theory like neural network, nonlinear model predictive control(NMPC) is realized on digital high speed computer. Predictor of flight control system(FCS) based nonlinear model predictive control has to be satisfied with response for hard real-time to perform applications on each module in the FCS. Simultaneously, It gives a serious consideration accuracy to give full play to FCS's performance. Error of mathematical aspect affects realization of whole algorithm. But factors of bring mathematical error is not considered to calculate final accuracy on parameter of predictor. In this paper, Predictor was made using load control model on the digital computer for design FCS at hard real-time and is shown response time on realization algorithm. And is shown realization algorithm of high effective predictor over the accuracy. The predictor was realized on the load control model using Euler method, Heun method, Runge-Kutta and Taylor method.

• Journal of Korean Society of Steel Construction
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• v.15 no.2
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• pp.197-206
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• 2003

The optimal design method in cooperation with a nonlinear elastic analysis method was presented. The proposed nonlinear elastic method overcame the drawback of the conventional LRFD method this approximately accounts for the nonlinear effect caused by using the moment amplification factors of and. The genetic algorithm uses a procedure based on the Darwinian notions of the survival of the fittest, where selection, crossover, and mutation operators are used to look for high performance among the sections of the database. They satisfy constraint functions and give the lightest weight to the structure. The objective function was set to the total weight of the steel structure. The constraint functions were load-carrying capacities, serviceability, and ductility requirement. Case studies for a two-dimensional frame, a three-dimensional frame, and a three-dimensional steel arch bridge were likewise presented.

• Journal of Korea Multimedia Society
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• v.10 no.10
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• pp.1284-1294
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• 2007

In this paper, a blind nonlinear channel equalization is implemented by using a Modified Fuzzy C-Means (MFCM) algorithm. The proposed MFCM searches the optimal channel output states of a nonlinear channel from the received symbols, based on the Bayesian likelihood fitness function instead of a conventional Euclidean distance measure. Next, the desired channel states of a nonlinear channel are constructed with the elements of estimated channel output states, and placed at the center of a Radial Basis Function (RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with that of a hybrid genetic algorithm (GA merged with simulated annealing (SA): GASA), and the relatively high accuracy and fast searching speed are achieved.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.382-388
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• 2007

In this paper, a modified Fuzzy C-Means (MFCM) algorithm is presented for nonlinear blind channel equalization. The proposed MFCM searches the optimal channel output states of a nonlinear channel from the received symbols, based on the Bayesian likelihood fitness function instead of a conventional Euclidean distance measure. Next, the desired channel states of a nonlinear channel are constructed with the elements of estimated channel output states, and placed at the center of a Radial Basis Function (RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with that of a hybrid genetic algorithm (GA merged with simulated annealing (SA): GASA), and the relatively high accuracy and fast searching speed are achieved.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.9
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• pp.775-780
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• 2016

In this paper, a computational algorithm of an improved and versatile structural analysis applicable for large-size flexible nonlinear structures is developed. In more detail, nonlinear finite element based on the co-rotational (CR) framework is developed. Then, a finite element tearing and interconnecting method using local Lagrange multipliers (FETI-local) is combined with the nonlinear CR finite element. The resulting computational algorithm is presented and applied for nonlinear static analyses, i.e., cantilevered beam and multibody structure. Finally, the proposed analysis is evaluated with regard to its parallel computation performance, and it is compared with those obtained by serial computation using the sparse matrix linear solver, PARDISO.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2E
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• pp.50-59
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• 1996

In this paper, an adaptive precompensator, which can reduce the distortion of a Wiener system effectively, is proposed. The previous techniques for adaptive precompensation, based on the Volterra series modeling to compensate the distortion of a nonlinear system, are not suitable for real-time implementation due to high computational burden and slow convergence burden and slow convergence rate. This paper presents an adaptive precompensation technique for the class of nonlinear subsystem, referred to as Wiener system. An adaptive algorithm for adjusting the parameters of a precompensator, structured by a hammerstein model, is derived using the stochastic gradient method. Also, an adaptive precompensatin technique which can effectively reduce nonlinear distortion in μ-law type of saturation characteristics is proposed. The validity of the proposed algorithm is confirmed through simulation by applying it to known Wiener systmes and a typical loudspeaker model.

• Proceedings of the KSME Conference
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• 2001.06d
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• pp.927-932
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• 2001

It is intended to develope an algorithm for dynamic simulation of fast-acting solenoid valves. The coupled equations of the electric, magnetic, and mechanical systems should be solved simultaneously in a transient nonlinear manner. The transient nonlinear electromagnetic field is analyzed by the Finite Element Method (FEM), which is coupled with nonlinear electronic circuitry. The dynamic movement of the solenoid valve is analyzed at every time step from the force balances acting on the plunger, which include the electromagnetic force calculated from the Finite Element analysis as well as the elastic force by a spring and the hydrodynamic pressure force along the flow passage. Dynamic responses of the solenoid valves predicted by this algorithm agree well with the experimental results including bouncing effects.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.342-345
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• 2003

This paper presents an adaptive PID control scheme for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model is used to estimate the error of control input, and the parameter of PID controller are adapted using the error. The parameters of TSK fuzzy model are also adapted to plant. The proposed algorithm allows designing adaptive PID controller which is adapted to the uncertainty of nonlinear plant and the change of parameters. The usefulness of the proposed algorithm is also certificated by the several simulations.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.6
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• pp.71-77
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• 1995

This paper deals with design method of learning fuzzy controller for control of an unknown nonlinear plant using the self-tuning algorithm of fuzzy inference rules. In this method the fuzzy identification model obtained that the joined identification model of nonlinear part and linear identification model of linear part by fuzzy inference systems. This fuzzy identification model ordered self-tuning by Decent method so as to be servile to nonlinear plant. A the end, designed learning fuzzy controller of fuzzy identification model have learning structure to model reference adaptive system. The simulation results show that th suggested identification and learning control schemes are practically feasible and effective.