• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.988-993
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• 2009

In this paper, we deal with a tracking gain-up controller design problem to control effectively the shake of tracking actuator after a track seek. A minimum tracking gain-up open-loop gain can be calculated by estimating the shake of tracking actuator and a desired transient specification is considered to diminish effectively the shake of actuator. A tracking gain-up controller is designed by considering a robust $H_{\infty}$ control problem with a regional stability constraint. The proposed tracking gain-up controller design method is applied to the track-following system of a DVD recording device and is evaluated through the experimental results.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.408-410
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• 2009

We present an efficient and robust measurement model for visual tracking. This approach builds on and extends work on subspace representations of measurement model. Subspace-based tracking algorithms have been introduced to visual tracking literature for a decade and show considerable tracking performance due to its robustness in matching. However the measures used in their measurement models are often restricted to few approaches. We propose a novel measure of object matching using Angle In Feature Space, which aims to improve the discriminability of matching in subspace. Therefore, our tracking algorithm can distinguish target from similar background clutters which often cause erroneous drift by conventional Distance From Feature Space measure. Experiments demonstrate the effectiveness of the proposed tracking algorithm under severe cluttered background.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3655-3671
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• 2015

In this paper, we develop a novel object tracking method based on sparse representation. First, we propose a relaxed sparse representation model, based on which the tracking problem is casted as an inverse sparse representation process. In this process, the target template is able to be sparsely approximated by all candidate samples. Second, we present an objective function that combines the sparse representation process of different fragments, the relaxed representation scheme and a weight reference prior. Based on some propositions, the proposed objective function can be solved by using an iteration algorithm. In addition, we design a tracking framework based on the proposed representation model and a simple online update manner. Finally, numerous experiments are conducted on some challenging sequences to compare our tracking method with some state-of-the-art ones. Both qualitative and quantitative results demonstrate that the proposed tracking method performs better than other competing algorithms.

• Smart Media Journal
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• v.7 no.4
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• pp.61-69
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• 2018

Multi-tracking of general objects and specific faces is an important topic in the field of computer vision applicable to many branches of industry such as biometrics, security, etc. The rapid development of deep neural networks has resulted in a dramatic improvement in face recognition and object detection problems, which helps improve the multiple-face tracking techniques exploiting the tracking-by-detection method. Our proposed method uses face detection trained with a head dataset to resolve the face deformation problem in the tracking process. Further, we use robust face features extracted from the deep face recognition network to match the tracklets with tracking faces using Hungarian matching method. We achieved promising results regarding the usage of deep face features and head detection in a face tracking benchmark.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.4
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• pp.319-328
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• 2010

This paper proposes a robust adaptive fuzzy tracking control scheme for a nonholonomic mobile robot with external disturbances as well as parameter uncertainties in the robot kinematics, the robot dynamics, and the actuator dynamics. In modeling a mobile robot, the actuator dynamics is integrated with the robot kinematics and dynamics so that the actuator input voltages are the control inputs. The presented controller is designed based on a FBFN (Fuzzy Basis Function Network) to approximate an unknown nonlinear dynamic function with the uncertainties, and a robust adaptive input to overcome the uncertainties. When the controller is designed, the different parameters for two actuator models in the actuator dynamics are taken into account. The proposed control scheme does not require the kinematic and dynamic parameters of the robot and actuators accurately. It can also alleviate the input chattering and overcome the unknown friction force. The stability of the closed-loop control system including the kinematic control system is guaranteed by using the Lyapunov stability theory and the presented adaptive laws. The validity and robustness of the proposed control scheme are shown through a computer simulation.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.264-269
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• 2002

In this paper, Η$_{\infty}$ depth and course controller of an AUV(Autonomous Underwater Vehicle) using Η$_{\infty}$ servo control is proposed. The Η$_{\infty}$ servo problem is formulated to design the controllers satisfying a robust tracking property with modeling errors and disturbances. The solution of the Η$_{\infty}$ servo problem is as fellows: first, this problem is modified as an Η$_{\infty}$ control problem for the generalized plant that includes a reference input mode, and then a sub-optimal solution that satisfies a given performance criteria is calculated by LMI(Linear Matrix Inequality) approach. The Η$_{\infty}$ depth and course controller are designed to satisfy with the robust stability about the modeling error generated from the perturbation of the hydrodynamic coefficients and the robust tracking property under disturbances(wave force, wave moment, tide). The performances of the designed controllers are evaluated with computer simulations, and finally these simulation results show the usefulness and application of the proposed Η$_{\infty}$ depth and course control system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.814-836
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• 2021

Wildlife ecologists and biologists recapture deer to collect tracking data from deer collars or wait for a drop-off of a deer collar construction that is automatically detached and disconnected. The research teams need to manage a base camp with medical trailers, helicopters, and airplanes to capture deer or wait for several months until the deer collar drops off of the deer's neck. We propose an intelligent robust base-station research with a low-cost and time saving method to obtain recording sensor data from their collars to a listener node, and readings are obtained without opening the weatherproof deer collar. We successfully designed the and implemented a robust base station system for automatically collecting data of the collars and listener motes in harsh wilderness environments. Intelligent solutions were also analyzed for improved data collections and pattern predictions with drone-based detection and tracking algorithms.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.969-980
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• 2018

This paper aims to design of the neutrosophic fuzzy-PID controller and it has been compared with the conventional fuzzy-PID controller for position tracking control in terms of robustness. In the neutrosophic fuzzy-PID controller, error (e) and change of error (ce) were assessed separately on two fuzzy inference systems (FISs). In this study, the designed method is different from the conventional fuzzy logic controller design, membership degrees of antecedent variables were determined by using the T(true), I(indeterminacy), and F(false) membership functions. These membership functions are grouped on the universe of discourse with the neutrosophic set approach. These methods were tested on three-dimensional (3-D) position-tracking control application of a spherical robot manipulator in the MATLAB Simulink. In all tests, reference trajectory was defined for movements of all axes of the robot manipulator. According to the results of the study, when the moment of inertia of the rotor is changed, less overshoot ratio and less oscillation are obtained in the neutrosophic fuzzy-PID controller. Thus, our suggested method is seen to be more robust than the fuzzy-PID controllers.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.67-77
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• 2014

An electric motor is the one of the most important parts in robot systems, which mainly drives the wheel of mobile robots or the joint of manipulators. According to the requirement of motor performance, the controller type and parameters vary. For the wheel driving motors, a speed tracking controller is used, while a position tracking controller is required for the joint driving motors. Moreover, if the mechanical parameters are changed or a different motor is used, we might have to tune again the controller parameters. However, for the beginners who are not familiar about the controller design, it is hard to design pertinently. In this paper, we develop a nominal robust controller model for the velocity tracking of wheel driving motors and the position tracking of joint driving motors based on the disturbance observer (DOB) which can reject disturbances, modeling errors, and dynamic parameter variations, and propose the methodology for the determining the least control parameters. The proposed control system enables the beginners to easily construct a controller for the newly designed robot system. The purpose of this paper is not to develop a new controller theory, but to increase the user-friendliness. Finally, simulation and experimental verification have performed through the actual wheel and joint driving motors.

• Journal of Communications and Networks
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• v.15 no.2
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• pp.164-172
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• 2013

We consider the problem of secure and robust clustering for quantized target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose a new method for jointly activating the best group of candidate sensors that participate in data aggregation, detecting the malicious sensors and estimating the target position. Firstly, we select the appropriate group in order to balance the energy dissipation and to provide the required data of the target in the WSN. This selection is also based on the transmission power between a sensor node and a cluster head. Secondly, we detect the malicious sensor nodes based on the information relevance of their measurements. Then, we estimate the target position using quantized variational filtering (QVF) algorithm. The selection of the candidate sensors group is based on multi-criteria function, which is computed by using the predicted target position provided by the QVF algorithm, while the malicious sensor nodes detection is based on Kullback-Leibler distance between the current target position distribution and the predicted sensor observation. The performance of the proposed method is validated by simulation results in target tracking for WSN.