• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1998년도 춘계학술대회논문집; 용평리조트 타워콘도, 21-22 May 1998
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• pp.261-266
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• 1998

In this study, the in-situ parameter identification method for active magnetic bearing (AMB) actuator based on an open-loop balancing scheme is proposed. The scheme utilizes the relation between the compensating voltage and the known unbalance force. Main advantage of this method is that it is easy to use, yet it gives the actuator dynamics on the actual operating condition of an AMB system. The experimental results show that the proposed scheme compensates the known unbalance accurately and consequently identifies the actuator dynamics effectively.

• 대한기계학회논문집
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• 제5권4호
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• pp.303-311
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• 1981

Machine tool dynamics is investigated under actual working conditions. Experimental evaluation of cutting dynamics in a lathe is made with cutting conditions and cutting positions varied. The thrust force and the toolpost and tailstock accelerations during turning process are modelled and analyzed by employing Dynamic Data System methodology. It is found that two acceleration signals are good enough to replace the thrust force, when used for machine tool identification under cutting process and for chatter detection.

• 로봇학회논문지
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• 제7권3호
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• pp.222-230
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• 2012

The paper presents a momentum-based regressor by using Hamiltonian dynamics representation for robotic manipulator. It has an advantage in that the proposed regressor does not require the acceleration measurement for the identification of dynamic parameters. Also, the identification algorithm is newly suggested by solving a minimization problem with constraint. The developed algorithm is easy to implement in real-time. Finally, the effectiveness of the proposed momentum-based regressor and identification method is shown through numerical simulations.

• 한국자동차공학회논문집
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• 제8권6호
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• pp.142-155
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• 2000

A MIMO model reference control scheme incorporating the variable structure theory for a vehicle four wheel steering system(4WS) is proposed and evaluated for a class of continuous-time nonlinear dynamics with known or unknown uncertainties. The scheme employs an neural network to identify the plant systems, where the neural network estimates the nonlinear dynamics of the plant. By the Lyapunov direct method, the algorithm is proven to be globally stable, with tracking errors converging to the neighborhood of zero. The merits of this scheme is that the global system stability is guaranteed and it is not necessary to know the exact structure of the system. With the resulting identification model which contains the neural networks, it does not need higher degrees of freedom vehicle model than 3 degree of freedom model. Th proposed scheme is applied to the active four wheel system and shows the validity is used to investigate vehicle handing performances. In simulation of the J-turn maneuver, the reduction of yaw rate overshoot of a typical mid-size car improved by 30% compared to a two wheel steering system(2WS) case, resulting that the proposed scheme gives faster yaw rate response and smaller side angle than the 2WS case.

• 동력기계공학회지
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• 제2권2호
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• pp.73-80
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• 1998

The identification of drilling joint dynamics which consists of drilling and structural dynamics and the on-line time series detection of malfunction process is substantial not only for the investigation of the static and dynamic characteristics but also for the analytic realization of diagnostic and control systems in drilling. Therefore, We have discussed on the comparative assessment of two recursive time series modeling algorithms that can represent the drilling operation and detect the abnormal geometric behaviors in precision roundshape machining such as turning, drilling and boring in precision diemaking. For this purpose, simulation and experimental work were performed to show the malfunctional behaviors for drilling operation. For this purpose, a new two recursive approach (Recursive Extended Instrument Variable Method : REIVM, Recursive Least Square Method : RLSM) may be adopted for the on-line system identification and monitoring of a malfunction behavior of drilling process, such as chipping, wear, chatter and hole lobe waviness.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.1812-1818
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• 2005

Even though SMA actuators have high power to volume ratio, there exist disadvantages such as hysteresis and saturation. So the model identification for SMA actuators is very difficult. For the qualitative model identification, we described the behavior of SMA actuators using a so-called diagonal model, which can readily expect the turning point of an incomplete phase transformation. For the quantitative model identification, we developed the general dynamics of SMA actuators using the modified Liang's model. Using this dynamics we can describe the hysteresis and the saturation very well. It is also very important to notice that the modified Liang's model maintains a continuous martensite fraction at the change of the phase transformation but the original model cannot.

• 대한전기학회논문지
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• 제35권6호
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• pp.246-256
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• 1986

Determination of an aerodynamic structure is a very important problem in missile modeling. The structure problem is to choose an appropriate set of aerodynamic coefficients to represent chosen missile dynamics. A methodology and criteria to determine a structure from windtunnel data are presented in this paper. Aerodynamic coeffecients in the determined structure are then identified by parameter identification algorithms. The identified coefficients are in turn used to verify appropriateness of the structure. The extended Kalman filter (EKF) and the maximum likelihood mithod (ML) are adopted as the parameter identification algorithm. Both methods exhibit satisfactory results. While the model identified by the ML more closely follows dynamics of the chosen missile than that by the EKF.

• 한국정밀공학회지
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• 제15권4호
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• pp.15-26
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• 1998

Full car model is needed for investigating as a entire dynamics of vehicle. In this study, 7DOF of full car model's dynamics is selected. This paper proposes the output feedback controller based on optimal control theory. Input data and output data from the optimal controller are used for neural network system identification of the suspension system. To do system identification, neural network which has robustness against nonlinearities and disturbances is adapted. This study uses back-propagation algorithm to train a multil-layer neural network. After obtaining a neural network model of a suspension system, a neuro-controller is designed. Neuro-controller controls suspension system with off-line learning method and multistep ahead prediction model based on the neural network model and a neuro-controller. The optimal controller and the neuro-controller are designed and then, both performances are compared through. For simulation, sinusoidal and rectangular virtual bumps are selected.

• 한국해양공학회지
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• 제8권2호
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• pp.131-140
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• 1994

Dynamics of AUV has heavy nonlinearities and many unknown parameters due to its bluff shape and low cruising speed. Intelligent algorithms, therefore, are required to overcome these nonlinearities and unknown system dynamics. Several identification techniques have been suggested for the application of control of underwater vehicles during last decade. This paper applies the neural network to identification and motion control problem of AUVs. Nonlinear dynamic systems of an AUV are identified using feedforward neural network. Simulation results show that the learned neural network can generate the motion of AUV. This paper, also, suggest an adaptive control scheme up-dates the controller weights with reference model and feedforward neural network using error back propagation.

• Structural Engineering and Mechanics
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• 제57권1호
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• pp.21-43
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• 2016

A conditional probability based approach known as Particle Filter Method (PFM) is a powerful tool for system parameter identification. In this paper, PFM has been applied to identify the vehicle parameters based on response statistics of the bridge. The flexibility of vehicle model has been considered in the formulation of bridge-vehicle interaction dynamics. The random unevenness of bridge has been idealized as non homogeneous random process in space. The simulated response has been contaminated with artificial noise to reflect the field condition. The performance of the identification system has been examined for various measurement location, vehicle velocity, bridge surface roughness factor, noise level and assumption of prior probability density. Identified vehicle parameters are found reasonably accurate and reconstructed interactive force time history with identified parameters closely matches with the simulated results. The study also reveals that crude assumption of prior probability density function does not end up with an incorrect estimate of parameters except requiring longer time for the iterative process to converge.