• 한국공작기계학회논문집
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• 제18권1호
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• pp.50-58
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• 2009

For precise tracking control of a servo system with nonlinear friction, a robust friction compensation scheme is presented in this paper. The nonlinear friction is difficult to identify the friction parameters exactly through experiments. Friction parameters can be also varied according to contact conditions such as the variation of temperature and lubrication. Thus, in order to overcome these problems and obtain the desired position tracking performance, a robust adaptive back-stepping control scheme with a dual friction observer is developed. In addition, to estimate lumped friction uncertainty due to modeling errors, a DEKF recurrent neural network and adaptive reconstructed error estimator are also developed. The feasibility of the proposed control scheme is verified through the experiment fur a ball-screw system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2275-2278
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• 2005

In this paper, a control algorithm for a servo manipulator is focused on. A servo manipulator system has been developed for remotely handling radioactive materials in a hot cell. It is driven by servo motors. The torque from a servo motor is transferred through a reducer to the corresponding axis. The PID control algorithm is a simple and effective algorithm for such application. However, since friction degrades the algorithm's performance, friction has to be considered and compensated. The major aberrations are the positional tracking errors and the limit cycle. The authors have considered a switching term to a conventional PID algorithm to reduce the friction's effect. It has been tested by a hardware test.

• 동력기계공학회지
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• 제12권1호
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• pp.80-86
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• 2008

A motor cylinder apparatus is used to transfer a load in industrial applications. The apparatus is composed of a motor and power transmission elements such as worm gear and screw. In this case, the nonlinear friction of the transmission elements has a bad influence on the position control performance. To overcome this problem, the position control system consists of a feedback controller to achieve nominal control performance and a disturbance observer to compensate nonlinear friction. Especially the filter of a disturbance observer is designed from viewpoint of robust stability. Finally, the simulation result shows that the proposed control system is effective for the disturbance elimination as well as the friction compensation.

• 대한기계학회논문집B
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• 제21권7호
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• pp.939-947
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• 1997

An experimental study of the skin friction measurement in a turbulent boundary-layer has been carried out. The skin friction measurements are made using the laser interferometer skin friction (LISF) meter, which optically detects the rate of thinning of an oil applied to the test surface. This technique produces reliable skin friction data over a wide range of flow situations up to 3-dimensional complicated flows with separation, where traditional skin friction measurement techniques are not applicable. The present measured data in a turbulent boundary-layer on a flat plate using the LISF technique shows a good comparison with the result from the previous velocity profile techniques, which proves the validity of the present technique. An extensive error analysis is carried out for the present technique yielding an uncertainty of about .+-.8%, which makes them suitable for CFD code validation purposes. Finally the measurements of the skin friction in a separated region after a surface-mounted obstacle are also presented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.452-455
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• 2003

Based on a field-oriented model of induction motor, adaptive backstepping approach using neural network(RBFN) is proposed for the control of induction motor in this paper. In order to achieve the speed regulation with the consideration of avoiding singularity and improving power efficiency, rotor angular speed and flux amplitude tracking objectives are formulated. rotor resistance uncertainty is compensated by adaptive backstepping and mechanical lumped uncertainty such as load torque disturbance, inertia moment, friction by RBFN. Simulation is provided to verify the effectiveness of the proposed approach.

• 제어로봇시스템학회논문지
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• 제1권2호
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• pp.88-93
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• 1995

This paper presents a study using neural networks in the design of the tracking controller of robotic systems. Our strategy is to put to use the available knowledge about the robot manipulator, such as estimation models, in the contoller design via the computed torque method, and then to add the neural network to control the remaining uncertainty. The neural network used here learns to provide the inverse dynamics of the plant uncertainty, and acts as an inverse controller. In the simulation study, we verify that the proposed neural network controller is robust not only to structured uncertainties, but also to unstructured uncertainties such as friction models.

• 한국정밀공학회지
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• 제12권5호
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• pp.117-125
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• 1995

An improved robust control law is proposed for uncertain rigid robots. The uncertainty is nonlinear and (possibly fast) time-varying. Therefore, the uncertain factors such as imperfect modeling, friction, payload change, and external disturbances are all addressed. Based on the possible bound of the uncertainty, the controller is constructed. For uncertain flexible-joint robots, some feedback control terms are then added to the proposed robust control law in order to stabilize the elastic vibrations at the joints. To show that the proposed control laws are indeed applicable, the stability study based on Lyapunov function, a singular perturbation approach, and simulation results are presented.

• Geomechanics and Engineering
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• 제3권2호
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• pp.153-167
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• 2011

Traditional design methods of bearing capacity of shallow foundations are deterministic in the sense that they do not explicitly consider the inherent uncertainty associated with the factors affecting bearing capacity. To account for such uncertainty, available deterministic methods rather employ a fixed global factor of safety that may lead to inappropriate bearing capacity predictions. An alternative stochastic approach is essential to provide a more rational estimation of bearing capacity. In this paper, the likely distribution of predicted bearing capacity of strip footings subjected to vertical loads is obtained using a stochastic approach based on the Monte Carlo simulation. The approach accounts for the uncertainty associated with the soil shear strength parameters: cohesion, c, and friction angle, ${\phi}$, and the cross correlation between c and ${\phi}$. A set of stochastic design charts that assure target reliability levels of 90% and 95%, are developed for routine use by practitioners. The charts negate the need for a factor of safety and provide a more reliable indication of what the actual bearing capacity might be.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.63-63
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• 2021

Bed shear stress is important variable in river flow analysis. The bed shear stress has an effects on bed erosion, sediment transport, and mean flow characteristics. Quadratic formula to estimate bed shear stress is widely used, 𝜏=𝜌c_fu|u| in which friction coefficient, c_f, needs to be assigned to numerical models. The aim of this study is to estimate Chezy coefficient using bathymetry data measured by ADCP. Bed form geometry variables will be estimated form bed profile, then Chezy coefficient will be determined using estimated bed form geometry variables in order to set friction coefficient to numerical model. From the probability density function obtained from the bathymetry data, Chezy coefficient will be randomly generated since Chezy coefficient is not uniform over the space and it does not depend on spatial variables such as water depth and distance from river bank. Numerical test will be performed to find to demonstrate randomly extracted Chezy coefficient is appropriate. The result of this study is valuable in that the friction coefficient is estimated in consideration of the bed profile, and as a result, uncertainty of the friction coefficient can be reduced.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.538-541
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• 1995

Joint flexibilities and frictional uncertainties are known to be a major cause of performance degration in motion control systems. This paper investigates the modeling and compensation of these undesired effects. A hybrid controller, which consists of a predictive controller and a neural network controller, is designed to overcome these undesired effects. Also learning scheme for friction uncertainies, which don't interfere with feedback controller dynamics, is discussed. Through simulation works with two inetia-torsional spring system having Coulomb friction, the effectiveness of the proposed hybrid controller was tested. The proposed predictive & neural network hybrid controller shows better performance over one when only predictive controller used.