• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.856-860
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• 2007

This paper presents a robust tracking controller design method for the track-following system of an optical recording device. A tracking loop gain adjustment algorithm is introduced to accurately estimate the tracking vibration quantity in spite of the uncertainties of the tracking actuator. A minimum tracking open-loop gain is calculated by the estimated tracking vibration quantity and a tolerable limit of tracking error. A robust tracking controller is designed by considering a robust $H_\infty$ control problem with the weighting function of a slightly larger gain than the minimum tracking open-loop gain. The proposed controller design method is applied to the track-following system of an optical recording device and is evaluated through the experimental result.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.426-429
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• 1997

This paper proposes a robust H$_{\infty}$ tracking filter to improve the unacceptable target tracking performance for systems with parameter uncertainties. Also, we use here the input estimation approach to account for the possibility of maneuver. Simulation results show that the robust H$_{\infty}$ tracking filter which is proposed here to solve the systems with all system parameter uncertainties, has a good tracking performance for a maneuvering target tracking problem.m.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.960-967
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• 2008

This paper shows that design of a robustly stable repetitive control system is equivalent to that of a feedback control system for an uncertain linear time-invariant system satisfying the well-known robust performance condition. Once a feedback controller is designed to satisfy the robust performance condition, the feedback controller and the repetitive controller using the performance weighting function robustly stabilizes the repetitive control system. It is also shown that we can obtain a steady-state tracking error described in a simple form without time-delay element if the robust stability condition is satisfied for the repetitive control system. Moreover, using this result, a sufficient condition is provided, which ensures that the least upper bound of the steady-state tracking error generated by the repetitive control system is less than or equal to the least upper bound of the steady-state tracking error only by the feedback system.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.4
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• pp.66-72
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• 2021

Since the behavior of an autonomous underwater vehicle (AUV) is influenced by disturbances and moments that are not accurately known, the depth control law of AUVs must have the ability to track the input signal and to reject disturbances simultaneously. Here, we proposed robust tracking control for controlling the depth of an AUV. An augmented closed-loop system is represented by an error dynamic equation, and we can easily show the asymptotic stability of the overall system by using a Lyapunov function. The robust tracking controller is consisted of the internal model of the command signal and a state feedback controller, and it has the ability to track the input signal and reject disturbances. The closed-loop control system is robust to parameter uncertainties. Simulation results showed the control performance of the robust tracking controller to be better than that of a P + PD controller.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.188-188
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• 2000

The most important problem in target tracking can be said to be modeling the tracking system correctly. Although the simple linear dynamic equation for this model has used until now, the satisfactory performance could not be obtained owing to uncertainties of the real systems in the case of designing the filters baged on the dynamic equations. In this paper, we propose the extended robust Kalman filter (ERKF) which can be applied to the real target tracking system with the parameter uncertainties. A nonlinear dynamic equation with parameter uncertainties is used to express the uncertain system model mathematically, and a measurement equation is represented by a nonlinear equation to show data from the radar in a Cartesian coordinate frame. To solve the robust nonlinear filtering problem, we derive the extended robust Kalman filter equation using the Krein space approach and sum quadratic constraint. We show the proposed filter has better performance than the existing extended Kalman filter (EKF) via 3-dimensional target tracking example.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2427-2433
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• 2015

This paper presents an observer-based robust controller for constant reference tracking of linear time invariant systems with polytopic model uncertainties. To this end, this paper not only designs a robust integral controller gain but also suggests how to determine the robust observer gain and the observer model used in the observer. Since the observer model selection is not obvious due to the polytopic uncertainties, particular attention needs to be paid to that. This paper computes the robust controller and observer gains first. Then, the observer model is selected in a way that the whole closedloop is stable and LMIs are used in the middle of choosing the gains and observer model. Simulation examples show that the proposed observer-based feedback control successfully achieves robust reference tracking.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.438-441
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• 2001

This paper describes a system for tracking a face in a input video sequence using facial convex hull based facial segmentation and a robust hausdorff distance. The algorithm adapts YCbCr color model for classifying face region by [l]. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, a Robust Hausdorff distance is computed and the best possible displacement is selected. Finally, the previous face model is updated using the displacement t. It is robust to some noises and outliers. We provide an example to illustrate the proposed tracking algorithm in video sequences obtained from CCD camera.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1955-1957
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• 2001

This paper propose the design method of robust tracking controller for nonlinear TS fuzzy system with uncertainties. The robust tracking controller design is presented by constraint of robust stability for nonlinear system. A sufficient condition of the robust stability is presented by LMI(Linear Matrix Inequality) soltuion in the sense of Lyapunov for TS fuzzy system with uncertainties. The effectiveness of the proposed robust tracking con design is demonstrated through a numerical simulatio.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1055-1058
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is the use of different model (CAD model etc.) known a priori. Also fusion or multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Voting-based fusion of cues is adapted. In voting. a very simple or no model is used for fusion. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters. namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• Annual Conference of KIPS
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• 2012.11a
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• pp.552-555
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• 2012

The main goal of this paper is to develop a robust visual tracking algorithm with particle filtering. Visual Tracking with particle filter technique is not easy task due to cluttered environment, illumination changes. To deal with these problems, we develop an efficient observation model for target tracking with particle filter. We develop a robust phase correlation combined with motion information based observation model for particle filter framework. Phase correlation provides straight-forward estimation of rigid translational motion between two images, which is based on the well-known Fourier shift property. Phase correlation has the advantage that it is not affected by any intensity or contrast differences between two images. On the other hand, motion cue is also very well known technique and widely used due to its simplicity. Therefore, we apply the phase correlation integrated with motion information in particle filter framework for robust tracking. In experimental results, we show that tracking with multiple cues based model provides more reliable performance than single cue.