• International Journal of Automotive Technology
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• v.8 no.6
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• pp.771-780
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• 2007

In recent studies, various types of multi mode electric variable transmissions of hybrid electric vehicles have been proposed. Multi mode electric variable transmission consists of two or more different types of planetary gear hybrid powertrain system(PGHP), which can change its power flow type by means of clutches for improving transmission efficiencies. Generally, the power flows can be classified into three different types such as input split, output split and compound split. In this study, we analyzed power transmission characteristics of the possible six input split systems, and found the suitable system for single or multi mode hybrid powertrain. The input split system used in PRIUS is identified as a best system for single mode, and moreover we identified some suitable systems for dual mode.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.1-8
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• 2005

A new discretization algorithm for nonlinear systems with delayed input is proposed. The algorithm is represented by Taylor series expansion and ZOH assumption. This method is applied to the sampled-data representation of a nonlinear system with the time-delay input. Additionally, the delay in input is time varying and its amplitude is bounded. The maximum time-delay in input is assumed to be two sampling periods. The mathematical expressions of the discretization method are presented and the ability of the algorithm is tested for some of the examples. The computer simulation proves the proposed algorithm discretizes the nonlinear system with the variable time-delay input accurately.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.157-165
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• 1995

In this paper the comparison between the neural networks and traditional approaches as nonlinear system identification methods are considered. Two model structures of neural networks are the state space model and the input output model neural networks. The traditional methods are the AutoRegressive eXogeneous Input model and the Nonlinear AutoRegressive eXogeneous Input model. Computer simulation for an analytic dynamic model of a single input single output nonlinear system has been done for all the chosen models. Model validation for the obtained models also has been done with testing inputs of the sinusoidal, ramp and the noise ramp.

• Wind and Structures
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• v.17 no.4
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• pp.451-464
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• 2013

This study develops an extended inverse input estimation algorithm with intelligent adaptive fuzzy weighting to effectively estimate the unknown input wind load of nonlinear structural systems. This algorithm combines the extended Kalman filter and recursive least squares estimator with intelligent adaptive fuzzy weighting. This study investigated the unknown input wind load applied on a tower structural system. Nonlinear characteristics will exist in various structural systems. The nonlinear characteristics are particularly more obvious when applying larger input wind load. Numerical simulation cases involving different input wind load types are studied in this paper. The simulation results verify the nonlinear characteristics of the structural system. This algorithm is effective in estimating unknown input wind loads.

• Journal of Power System Engineering
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• v.3 no.4
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• pp.69-78
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• 1999

In the single-input and single-output system, the parameter of plant is scalar polynomial, but in the multiple input and multiple output, it accompanies, being matrix polynomial, the consideration of observable controlability index or problems non-commutation in matrix polynomial as well as degree, and it is more complex to deal with. Therefore, it is thought that a full research on the single-input and single-output system is not sufficient. This paper proposes that problems of minimum variance self-tuning regulator by using numerical calculation example of multivariable system and pole assignment self-tuning regulator.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.380-383
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• 2003

This paper proposes a new and simple input prediction method for robust servo system. This servo system uses robust tracking control system based on both Coprime Factorization(CF) and Zero Phase Error Tracking control system. The CF control system can be designed simply and systematically. Moreover, this system has not only stability but also robustness and disturbance rejection ability The optical disk tracking servo system can detect only the tracking error. So the new and simple input prediction system proposed in this paper estimates the reference input signal from the tracking error. Numerical simulation results show that the proposed method is effective.

• Journal of Power System Engineering
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• v.6 no.3
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• pp.49-56
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• 2002

This study suggests a trajectory tracking controller composed of an input output linearization compensator and a linear controller. The input output linearization compensator is derived from the nonlinear equations of a pneumatic control system and it algebraically transforms a nonlinear system dynamics into a linear one, so that input output characteristics of the control system is linearized regardless of the variation of the operating point and linear control techniques can be applied. The results of nonlinear simulations show that the proposed controller tracks the given trajectories more accurately than a state feedback controller does.

• Structural Engineering and Mechanics
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• v.27 no.5
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• pp.527-540
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• 2007

The modal controller of single-input system cannot stabilize the defective system with positive real part of repeated eigenvalues, because some of the generalized modes are uncontrollable. In order to stabilize the uncontrollable modes with positive real part of eigenvalues, the multi-input system should be introduced. This paper presents a recursive procedure for designing the feedback controller of the multi-input system with defective repeated eigenvalues. For a nearly defective system, we first transform it into a defective one, and apply the same method to manage. The proposed methods are based on the modal coordinate equations, to avoid the tedious mathematic manipulation. As an application of the presented procedure, two numerical examples are given at end of the paper.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2562-2567
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• 2005

This paper suggests a new method discretization of nonlinear system using Taylor series expansion and zero-order hold assumption. This method is applied into the sampled-data representation of a nonlinear system with input time delay. Additionally, the delayed input is time varying and its amplitude is bounded. The maximum time-delayed input is assumed to be two sampling periods. Them mathematical expressions of the discretization method are presented and the ability of the algorithm is tested for some of the examples. And 'hybrid' discretization scheme that result from a combination of the ‘scaling and squaring' technique with the Taylor method are also proposed, especially under condition of very low sampling rates. The computer simulation proves the proposed algorithm discretized the nonlinear system with the variable time-delayed input accurately.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.1013-1018
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• 2003

This paper deals with the secondary excited induction generator applied to random energy input generation system. As it is preferred to stabilize the output voltage and frequency in the constant level, microcomputer controlled inverter connected to the secondary windings supplies the secondary current with slip frequency. For testing the appropriateness of this paper, the input torque simulator, which generate the statistically varied wave power input torque in the laboratory to drive the secondary excited induction generator, are constructed. The experimental and numerical results show the advantage of secondary excited induction generator system for the random input wave generation system.