• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.980-984
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• 1990

In order to find the optimal control law for the precise trajectory tracking of a robot manipulator, a perturbational control method is proposed based on a linearized manipulator dynamic model which can be obtained in a very compact and computationally efficient manner using the dual number algebra. Manipulator control can be decomposed into two parts: the nominal control and the corrective perturbational control. The nominal control is precomputed from the inverse dynamic model using the quantities of a desired trajectory. The perturbational control is obtained by applying the second-variational method on the linearized dynamic model. Simulation results for a PUMA-560 robot show that, by using this controller, the desired trajectory tracking performance of the robot can be achieved, even in the presence of large initial positional disturbances.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.317-323
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• 1987

A pressure tracking control of hydroforming processes, which is used in the precision forming of. sheet metals, is considered in this paper. The hydroforming of sheet metal is performed between the high-pressure chamber controlled by pressure control valve and the punch moving with constant speed. Since the pressure in the forming chamber is a critical factor to the quality of the product severely. It is important to control the pressure to follow a prescribed pressure trajectory, depending upon the material volume and shape of the parts to be formed. Taking into consideration of the volume chamge of forming chamber during the process and the nonlinearity of the electro-magnetic relief valve, a mathematical formulation of the model describing the dynamic characteristics of this model obtained. Based upon this model a PID controller is designed for the pressure tracking.

• Proceedings of the KIEE Conference
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• 2002.11d
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• pp.286-293
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• 2002

Photovoltaic systems normally use a maximum power point tracking (MPPT) technique to continuously deliver the highest possible power to the load when variations in the insolation and temperature occur. A simple method of tracking the maximum power points (MPPs) and forcing the boost converter system to operate close to these Points is presented through deriving small-signal model and transfer function of boost converter. This paper aims at modeling boost converter including equivalent series resistance of input reservoir capacitor by state-space-averaging method and PWM switch model. In the future, properly designed controller for compensation will be constructed in real system for maximum photovoltaic power tracking control.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.307-316
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• 2007

In real system application, the interacting multiple model (IMM) based algorithm operates with the following problems: it requires less computing resources as well as a good performance with respect to the various target maneuvering, it requires a robust performance with respect to the time-varying measurement noise, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an adaptive fuzzy interacting multiple model (AFIMM) algorithm, which is based on the basis sub-models defined by considering the maneuvering property and the time-varying mode transition probabilities designed by using the mode probabilities as the inputs of the fuzzy decision maker whose widths are adjusted, is proposed. To verify the performance of the proposed algorithm, a radar target tracking is performed. Simulation results show that the proposed AFIMM algorithm solves all problems in the real system application of the IMM based algorithm.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.5
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• pp.609-616
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• 2003

The purpose of this study is to design the adaptive speed control system of a marine diesel engine by combining the Model Matching Method and the Nominal Model Tracking Method. The authors proposed already a new method to determine efficiently the PID control Parameters by the Model Matching Method. typically taking a marine diesel engine as a non-oscillatory second-order system. But. actually it is very difficult to find out the exact model of a diesel engine. Therefore, when diesel engine model and actual diesel engine are unmatched as an another approach to promote the speed control characteristics of a marine diesel engine, this paper Proposes a Model Reference Adaptive Speed Control system of a diesel engine, in which PID control system for the model of a diesel engine is adopted as the nominal model and Fuzzy controller and derivative operator are adopted as the adaptive controller.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.700-703
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• 1997

A robust controller for a 6 DOF magnetically levitated fine manipulator is presented. The proposed controller consists of following two parts : a model reference controller (MRC) and a H$_{\infty}$ controller (HIC). First, the MRC stabilizes the motion of the manipulator. Then, the motion of the manipulator follows that of the reference model. Second, the HIC minimizes errors generated from the MRC due to noise and disturbance since the HIC is a kind of robust controller. The experiments of position control and tracking control are carried out by use of the proposed controller under the conditions of free disturbances and forced disturbances. Also, the experiments using PID controller are carried out under the same conditions. The results from above two controllers are compared to investigate the control performances. As the results, it is observed that the proposed controller has similar position accuracy but better tracking performances comparing to the PID controller as well as good disturbance rejection effect due to the robust characteristics of the controller. In conclusion, it is verified that the proposed controller has the simple control structure, the good tracking performances and good disturbance rejection effect due to the robust characteristics of the controller..

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.2
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• pp.78-87
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• 2012

In this paper, we address the tracking problem caused by camera motion and rolling shutter effects associated with CMOS sensors in consumer handheld cameras, such as mobile cameras, digital cameras, and digital camcorders. A modified particle filtering method is proposed for simultaneously tracking objects and compensating for the effects of camera motion. The proposed method uses an elastic registration algorithm (ER) that considers the global affine motion as well as the brightness and contrast between images, assuming that camera motion results in an affine transform of the image between two successive frames. By assuming that the camera motion is modeled globally by an affine transform, only the global affine model instead of the local model was considered. Only the brightness parameter was used in intensity variation. The contrast parameters used in the original ER algorithm were ignored because the change in illumination is small enough between temporally adjacent frames. The proposed particle filtering consists of the following four steps: (i) prediction step, (ii) compensating prediction state error based on camera motion estimation, (iii) update step and (iv) re-sampling step. A larger number of particles are needed when camera motion generates a prediction state error of an object at the prediction step. The proposed method robustly tracks the object of interest by compensating for the prediction state error using the affine motion model estimated from ER. Experimental results show that the proposed method outperforms the conventional particle filter, and can track moving objects robustly in consumer handheld imaging devices.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.8
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• pp.719-726
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• 2015

This paper presents a velocity-synchronizing control for the multiple axes of an injection unit; based on MBS, a virtual design model has been developed for the multiple-axes servomechanism. Prior to the design of the controller, a linear plant model was derived via open-loop response simulations. To synchronize the motions of the multiple axes, a cross-type synchronizing controller was designed and combined with the PID control to accommodate any parameter mismatches among the multiple axes. From the tracking control simulations, a significant reduction of both velocity-tracking and position-tracking errors was achieved through the use of the proposed control scheme.

• Journal of Welding and Joining
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• v.10 no.4
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• pp.190-198
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• 1992

Among the variety of welding processes available, the flux cored arc welding is one of the most frequently used process, because of its wide range of application and high productivity. The weld joint tracking is indispensable to improve the flexibility of the arc welding robot application for the flux cored arc welding (FCAW) process. In this study, an arc sensor which utilizes the electrical signal obtained from the welding arc itself was developed for weld joint tracking in FCAW. Because a model of the welding arc in flux cored arc welding was required to develop the arc sensor, a mathematical model was proposed by analysing the welding arc behaviour, and also an experimental model by using the factorial experiment and least square method. For overcoming the fluctuation in the welding current signal during tracking the weld joint, it was fitted to a curve which is inversely proportional to a trace of tip-to-workpiece distance by using the quadratic curve-fitting method.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.467-473
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• 2007

In this paper, we present a position-based robot hand tracking scheme where a pan-tilt camera is controlled such that a robot hand is always shown in the center of an image frame. We calculate the rotation angles of a pan-tilt camera by transforming the coordinate systems. In order to identify the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. From the simulation results, it is shown that the considered parameter identification problem is characterized by a highly multimodal landscape; thus, a global optimization technique such as a particle swarm optimization could be a promising tool to identify the model parameters of a robot hand tracking system, whereas the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum.