• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 춘계학술대회논문집
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• pp.824-829
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• 2002

Since many reciprocating and rotating components are attached to jet loom structure. it is exposed to a more vibration and moise problems than the other textile machinery. Thus the design of the jet loom frame is very important to characterize the dynamic response. In this study, a finite element model of jet loom main frame was developed to investigate the dynamic characteristics of jet loom. Two different finite element models of different main frames were constructed and these models were validated by the experimental results. Dynamic characteristics such natural frequencies and mode shapes were in good agreement between the finite element analysis and experimental results within 10% error range. It is expected that the result from this study can be used as the basic information of jet loom dynamic analysis and be extended for further analysis of forced response case.

• 제어로봇시스템학회논문지
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• 제15권7호
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• pp.711-719
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• 2009

This paper presents a design scheme of torque control for wheeled mobile robot(WMR) to asymptotically track the target reference trajectory. By considering the kinematic model of WMR, trajectory tracking control generates the desired tracking trajectory, which is transformed into the command velocity vector for the real WMR to track the target reference trajectory. The dynamic equation of the state error between the target reference trajectory and the desired tracking trajectory is represented by Takagi-Sugeno fuzzy model, and this model is used as the reference model for the real mobile robot error dynamics to follow. The control parameters are updated by adaptive laws that are designed for the error states of the real WMR to asymptotically follow the states of reference error model for the desired tracking trajectory. The proposed control is applied to a typical wheeled mobile robot and simulation studies are carried out to verify the validity and effectiveness of the control scheme.

• Earthquakes and Structures
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• 제25권3호
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• pp.161-175
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• 2023

High-damping rubber bearings (HDRB) are commonly used as seismic isolation devices to protect civil engineering structures from earthquakes. However, the nonlinear hysteresis characteristics of the HDRB, such as their dependence on material properties and hardening phenomena, make predicting their behavior during earthquakes difficult. This study proposes a hysteretic model that can accurately predicts the behavior of shear deformation considering the nonlinearity when designing the seismic isolation structures using HDR bearings. To model the hysteretic characteristics of the HDR, dynamic loading tests were performed by applying sinusoidal and random waves on scaled-down specimens. The test results show that the nonlinear characteristics of the HDR strongly correlate with the shear strain experienced in the past. Furthermore, when shear deformation occurred above a certain level, the hardening phenomenon, wherein the stiffness increased rapidly, was confirmed. Based on the experimental results, the dynamic characteristics of the HDR, equivalent stiffness, equivalent damping ratio, and strain energy were quantitatively evaluated and analyzed. In this study, an improved bilinear HDR model that can reproduce the dependence on shear deformation and hardening phenomena was developed. Additionally, by proposing an objective parameter-setting procedure based on the experimental results, the model was devised such that similar parameters could be set by anyone. Further, an actual dynamic analysis could be performed by modeling with minimal parameters. The proposed model corresponded with the experimental results and successfully reproduced the mechanical characteristics evaluated from experimental results within an error margin of 10%.

• 대한인간공학회지
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• 제1권2호
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• pp.3-9
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• 1982

This study demonstrates that certain personal and task factors can be modelled to predict slip potential as well as back loadings durings dynamic pushing and pulling tasks. Such tasks are com- mon to many manual material handling jobs in industry and the results of this work will hopefully be of use in improved job design. The objective of this research is to formulate and validate a dynamic biomechanical model of pushing and pulling a cart. For pushing and pulling tasks, the model can : (1) estimate foot forces for given hand forces, and (2) estimate tors muscle and vertabral column loadings. In order to formulate and validate the model, experiments involving pushing and pulling of a cart were performed. These experiments produced data of the following type : (1) dynamic forces on the feet, (2) hand forces required to move the cart, (3) body motions as functions of various cart motion and (4) back muscle actions. The model was validated using three different methods; precision was tested using correlation between predicted and measured results, accuracy using standard error between of predicted and measured results, and intuitive comparison of predicted results using sensitivity analyses.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2188-2190
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• 2004

This paper presents an implementation of power system stabilizer using inverse dynamic neuro controller. Traditionally, mutilayer neural network is used for a universal approximator and applied to a system as a neuro-controller. In this case, at least two neural networks are used and continuous tuning of neuro-controller is required. Moreover, training of neural network is required considering all possible disturbances, which is impractical in real situation. In this paper, Taylor Model Based Inverse Dynamic Neuro Model (TMBIDNM) is introduced to avoid this problem. Inverse Dynamic Neuro Controller (IDNC) consists of TMBIDNM and Error Reduction Neuro Model (ERNM). Once the TMBIDNM is trained, it does not require retuning for cases with other types of disturbances. The controller is tested for one machine and infinite-bus power system for various operating conditions.

• 한국정밀공학회지
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• 제12권6호
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• pp.145-151
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• 1995

The first-order Taylor series expansion can be evaluated analytically from the formulated symbolic nonlinear dynamic equations. A closed-form linear dynamic euation is derived about a nominal trajectory. The state space representation of the linearized dynamics can be derived easily from the closed-form linear dynamic equations. But manual symbolic expansion of dynamic equations and linearization is tedious, time-consuming and error-prone. So it is desirable to manipulate the procedures using a computer. In this paper, the analytic linearization is performed using the symbolic language MATHEMATICA. Two examples are given to illustrate the approach anbd to compare nonlinear model with linear model.

• 대한의용생체공학회:의공학회지
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• 제31권4호
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• pp.310-315
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• 2010

Estimation of muscle forces is important in biomechanics, therefore many researchers have tried to build a muscle model. Recently, optimization techniques for adjusting muscle parameters, i.e. EMG-driven model, have been used to estimate muscle forces and predict joint moments. In this study, an EMG-driven model based on the previous studies has been developed and isometric and isokinetic contraction movements were evaluated to validate the developed model. One healthy male participated in this study. The dynamometer tasks were performed for maximum voluntary isometric contractions (MVIC) for ankle dorsi/plantarflexors, isokinetic contraction at both $30^{\circ}/s$ and $60^{\circ}/s$. EMGs were recorded from the tibialis anterior, gastrocnemius medialis, gastrocnemius lateralis and soleus muscles at the sampling rate of 1000 Hz. The MVIC trial was used to customize the EMG-driven model to the specific subject. Once the subject's own model was developed, the model was used to predict the ankle joint moment for the other two dynamic movements. When no optimization was applied to characterize the muscle parameters, weak correlations were observed between the model prediction and the measured joint moment with large RMS error over 100% (r = 0.468 (123%) and r = 0.060 (159%) in $30^{\circ}/s$ and $60^{\circ}/s$ dynamic movements, respectively). However, once optimization was applied to adjust the muscle parameters, the predicted joint moment was highly similar to the measured joint moment with relatively small RMS error below 40% (r = 0.955 (21%) and r = 0.819 (36%) and in $30^{\circ}/s$ and $60^{\circ}/s$ dynamic movements, respectively). We expect that our EMG-driven model will be employed in our future efforts to estimate muscle forces of the elderly.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 춘계학술대회 논문집
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• pp.483-487
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• 1996

We consider the estimation of the position and orientation of 6 DOF motion bed (Stewart platform) from the measured cylinder length. The solution of forward kinematics is not solved yet as a useful realtime application tool because of the complity of the equation with multiple solutiple solutions. Hence we suggest an algorithm for the estimation of forward kinematics solution using Luenberger observer withnonlinear error correction term. The Luenberger observer withlinear model shows that the estimation error does not go to zero in steadystate due to the linearization error of the dynamic model. Hence the linear observer is modified using nonlinear measurement error equation and we prove thd practical stability of the estimation error dynamics of the proposed observer using lyapunov function.

• Journal of Electrical Engineering and Technology
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• 제8권5호
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• pp.975-983
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• 2013

This study proposes a low order dynamic model of a building system in order to predict thermal behavior within a building and its energy consumption. The building system includes a thermally well-insulated room and an electric heater. It is modeled by a second order lumped RC thermal network based on the thermal-electrical analogy. In order to identify unknown parameters of the model, an experimental procedure is firstly detailed. Then, the different linear parametric models (ARMA, ARX, ARMAX, BJ, and OE models) are recalled. The parameters of the parametric models are obtained by the least square approach. The obtained parameters are interpreted to the parameters of the physically based model in accordance with their relationship. Afterwards, the obtained models are implemented in Matlab/Simulink(R) and are evaluated by the mean of the sum of absolute error (MAE) and the mean of the sum of square error (MSE) with the variable of indoor temperature of the room. Quantities of electrical energy and converted thermal energy are also compared. This study will permit a further study on Model Predictive Control adapting to the proposed model in order to reduce energy consumption of the building.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.7-11
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• 1991

The adaptive control of flexible joint manipulator is the focus of this paper. The full order flexible joint manipulator dynamic system does not allow the determination of a feedback linearization control as for rigid manipulators. This drawback is overcome by a model order reduction based on a singular perturbation strategy. The full order flexible joint manipulator dynamic model is adopted for derivation of the adaptive control law to damp out the elastic oscillations at the joints. It is shown that the joint position error will converge to zero asymptotically and that other signals remain bounded without precise knowledge of parameters of the manipulator and its joint flexibility.