• 제어로봇시스템학회논문지
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• 제2권3호
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• pp.181-187
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• 1996

A frictionless positioning device using cone-shaped active magnetic bearings(AMBs) is developed, which is driven by a brushless DC motor equipped with resolver. The cone-shaped AMB feature that the structure is simple and yet the five d.o.f. rotor motion is controlled by four magnet pairs. A linearized dynamic model, which accounts for the relationship between input voltage and output current in the cone-shaped magnet, is developed and the azimuth motion of the frictionless positioning device is modeled as the second order system. The feedback controller is designed by using linear quadratic regulator with integral action optimal control law so that the cone-shaped AMB system is stabilized and the frictionless positioning device gets the zero steady state. It is observed that the linearized dynamic model is adequate and the frictionless positioning device can achieve the tracking accuracy within the sensor resolution.

• 한국정밀공학회지
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• 제17권8호
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• pp.78-86
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• 2000

This study identified a linear time-invariant mathematical model of each stand of a five-stand tandem cold mill. Two model identification methods are applied to construct a linear model of each stand of the tandem cold mill. For the model identification the input-output data that have interstand interference property in tandem cold rolling are obtained from a nonlinear simulator of the tandem cold mill. And a linear model of each stand is identified with N4SD(numerical algorithms for subspace state space system identification) method based on a state-space model and Least Square algorithm based on a transfer function. Furthermore a modeling error of the tandem cold mill is quantitatively analyzed from a maximum singular value plot of error function between an identified nominal model and uncertain model. In conclusion the comparison of the output signals between the existing Taylor linearized model the identified linear model and the nonlinear model of the tandem cold mill shows the accuracy and the applicability of the proposed identified model.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1997년도 전력전자학술대회 논문집
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• pp.70-78
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• 1997

A DSP-based nonlinear speed control of a permanent magnet synchronous motor(PMSM) which is robust to unknown parameter variations and speed measurement error is presented. The model reference adaptive system(MRAS) based adaptation mechanisms for the estimation of slowly varying parameters are derived using the Lyapunov stability theory. For the disturbances or quickly varying parameters, a quasi-linearized and decoupled model including the influence of parameter variations and speed measurement error on the nonlinear speed control of the PMSM is derived. Based on this model, a boundary layer integral sliding mode controller to improve the robustness and performance of a PMSM drive is designed and compared with the conventional controller. To show the validity of the proposed control scheme, simulations and experimental works are carried out and compared with the conventional control scheme.

• 한국군사과학기술학회지
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• 제4권1호
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• pp.225-233
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• 2001

This paper deals with the transfer alignment problem of SDINS(StrapDown Inertial Navigation System) subjected to roll and pitch motions of the ship. Specifically, to reduce alignment errors induced by measurement time-delay and ship body flexure, an error compensation method is suggested based on delay state augmentation and DCM(Direction Cosine Matrix) partial matching. A linearized error model for the velocity and attitude matching transfer alignment system is first derived by linearizing the nonlinear measurement equation with respect to its time delay and augmenting the delay state into the conventional linear state equations. And then DCM partial matching is properly combined to reduce effects of a ship's Y axis flexure. The simulation results show that the suggested method is effective enough resulting in considerably less azimuth alignment errors.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2000년도 전력전자학술대회 논문집
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• pp.620-623
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• 2000

A very simple control approach using neural network for the robust position control of a Permanent Magnet Synchronous Motor(PMSM) is presented The linear quadratic controller plus feedforward neural network is employed to obtain the robust PMSM system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a fedforward recall and error back-propagation training. Since the total number of nodes are only eight this system can be easily realized by the general microprocessor. During the normal operation the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. IN addition the robustness is also obtained without affecting overall system response. This method is realized by a floating-point Digital Singal Processor DS1102 Board (TMS320C31) The basic DSP software is used to write C program which is compiled by using ANSI-C style function prototypes.

• 한국정밀공학회지
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• 제13권10호
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• pp.123-129
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• 1996

Computed torque method has been used for precise trajectory control of the robotic system that involves nonlinear dynamics. It is hard to know exact values of robot system parameters, and the robot arm receives umpredictable interference from the working environment. These disturbances, especially in a direct drive robot, are directly transmitted to actuating motor without reduction. Modelling error and distrubance can cause significant errors in a trajectory tracking problem. In this paper, we propose a new controller that $H_{\infty}$controller is conbined to robot system linearized by computed torque. Simula- tions are made for comparing the performance of the proposed controller with that of a nonlinear $H_{\infty}$ controller proposed by Chen and also computed torque method.hod.

• Journal of Electrical Engineering and information Science
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• 제3권2호
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• pp.251-260
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• 1998

A DSP-based robust nonlinear speed control of a permanent magnet synchronous motor(PMSM) which is robust to unknown parameter variations and speed measurement error is presented. The model reference adaptive system(MRAS) based adaptation mechanisms for the estimation of slowly varying parameters are derived using the Lyapunov stability theory. For the disturbances or quickly varying parameters. a quasi-linearized and decoupled model including the influence of parameter variations and speed measurement error on the nonlinear speed control of a PMSM is derived. Based on this model, a boundary layer integral sliding mode controller to improve the robustness and performance of the nonlinear speed control of a PMSM is designed and compared with the conventional controller. To show the validity of the proposed control scheme, simulations and experimental works are carried out and compared with the conventional control scheme.

• 제어로봇시스템학회논문지
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• 제8권2호
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• pp.178-184
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• 2002

This paper deals with the transfer alignment problem of SDINS(StrapDown Inertial Navigation System) subjected to roll and pitch motions of the ship. In order to reduce alignment errors induced by ship body flexure, a linearized error model for the velocity and attitude matching transfer alignment system is first derived by linearizing the nonlinear measurement equation with respect to the dominant y axis component and defining the flexure state of random constant type. And then a robust state estimation scheme is introduced to account for modeling uncertainty of the flexure. By interpreting the simulation results and comparing with the velocity and DCM(Direction Cosine Matrix) partial matching method, it is shown that the proposed method is effective enough to improve the azimuth alignment performance.

• 제어로봇시스템학회논문지
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• 제8권11호
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• pp.984-990
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• 2002

We propose a new type of nonlinear fixed interval smoother to which an existing nonlinear smoother is modified. The nonlinear smoother is derived from two-filter formulas. For the backward filter. the propagation and the update equation of error states are derived. In particular, the modified update equation of the backward filter uses the estimated error terms from the forward filter. Data fusion algorithm, which combines the forward filter result and the backward filter result, is altered into the compatible form with the new type of the backward filter. The proposed algorithm is more efficient than the existing one because propagation in backward filter is very simple from the implementation point of view. We apply the proposed nonlinear smoothing algorithm to off-line navigation system and show the proposed algorithm estimates position, and altitude fairly well through the computer simulation.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2002년도 추계학술대회 논문집
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• pp.53-56
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• 2002

This paper presents neural observer that used to deadbeat load torque observer. Most practical systems are nonlinear, and it is general practice to use linear models to simplify their analysis and design. However, the locally linearized model is invalid for a large signal change. The neural observer is suggested to increase the performance of the load torque observer and main controller The output error and estimeted state is trianed by neural network of neural observer. As a result, the state estimation error is minimised and deadbeat load torque observer make use of corrected esimation state. To reduce of the noise effect of deadbeat load torque observer, the post-filter which is implemented by MA process, is adopted. As a result, the proposed control system becomes a robust and precise system against the load torque. A stability and usefulness, through the verified computer simulation, are shown in this paper.