• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.2
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• pp.96-104
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• 2000

In this paper, an $H_{\infty}$ robust controller has been designed for a large-scale system consisted of subsystems of mutually coupled plants. The physical plant, a two-dimensional horizontal movement robot system, has two subsystem plants mutually coupled by links. The designed $H_{\infty}$controller has been designed to get not only the robust stability for exogenous inputs to each plant but also the good tracking performance for the reference input to each plant. The $H_{\infty}$controller has shown the superior tracking performance and robust stability compared with the proportional-plus-derivative controller through computer simulations and physical experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.389-396
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• 2016

The sealing robot must be able to calculate the slope of a contact surface for complete adherence of the sealing on different concrete shapes. After the slope is obtained, the robot will track on the surface of the concrete, but this process contains an error in the actual purpose of the force command. The reason this a phenomenon occurs, the non-linearity of the contact surface and the end-effector, is due to parasitic coupling. Errors like make it difficult to measure accurately the respective factors. Therefore, it is regarded as a disturbance that occurs when it follows the work surface it. In this paper, we selected the friction coefficient of the surface as a control factor and designed a compensator to reduce effects of disturbance. Finally, in view of the non-linearity of the end-effector of a robot to contact surfaces directly, we propose a robust force tracking controller in the finite range for managing disturbances that occur during the sealing.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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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.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1006-1018
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• 2020

Dataset is getting more import to develop a learning based algorithm. Quality of the algorithm definitely depends on dataset. So we introduce new dataset over 200 thousands images which are fully labeled multi modal sensor data. Proposed dataset was designed and constructed for researchers who want to develop detection, tracking, and action classification in outdoor environment for surveillance scenarios. The dataset includes various images and multi modal sensor data under different weather and lighting condition. Therefor, we hope it will be very helpful to develop more robust algorithm for systems equipped with difference kinds of sensors in outdoor application. Case studies with the proposed dataset are also discussed in this paper.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.773-780
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• 1999

A digital tracking controller is proposed for micro electrostatic actuator with input nonlinearity, where disturbance observer is utilized in cooperation with inverse function. Generally the disturbance observer is announced to be robust to modeling uncertainty, and external disturbance. But, when the nonlinearity exists in the systems, the disturbance observer may not directly be applied to that system, because the nonlinearity may destabilize the overall system. Therefore, first, we linearize the nonlinear input characteristics of micro electrostatic actuator by the use of inverse function. Secondly, we apply disturbance observer to approximately linearized system for eliminating the residuals of nonlinearity and the modeling uncertainty. Then, we get the good properties of the disturbance rejection as well as the robustness due to the own nature of disturbance observer. In this case, we propose a sufficient condition for the robust stability of overall systems. Furthermore, we discuss the problem that may be exposed when disturbance observer is applied to the internally stable system with saturation, and analyze two methods to overcome input saturation problem in the sense of internal stability. Simulations have been carried out to show the effectiveness of the proposed controller.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.217-221
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• 2007

This paper deals with the robust nonlinear controller design using output feedback for a Chua circuit which is one of the well-known nonlinear models. First, an exosystem for reference signal tracking is defined, and error dynamic equations are derived from the differentiation of the output tracking error equation. The normal sliding surface is modified using the integral type servo compensator. The parameters in the equations of the modified sliding surface and servo compensator are determined by using the Hurwitz condition of stability. Especially the error signals can't be obtained directly from the output because all parameters are assumed unknown. So instead, a high gain observer is designed. From this estimated error signals, a stabilizing controller is designed. Simulation is done for demonstrating the effectiveness of the suggested algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.1
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• pp.77-85
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• 2018

This paper proposes a robust digital video stabilization algorithm to extract and track an object, which uses a phase correlation-based motion correction. The proposed video stabilization algorithm consists of background stabilization based on motion estimation and extraction of a moving object. The motion vectors can be estimated by calculating the phase correlation of a series of frames in the eight sub-images, which are located in the corner of the video. The global motion vector can be estimated and the image can be compensated by using the multiple local motions of sub-images. Through the calculations of the phase correlation, the motion of the background can be subtracted from the former frame and the compensated frame, which share the same background. The moving objects in the video can also be extracted. In this paper, calculating the phase correlation to track the robust motion vectors results in the compensation of vibrations, such as movement, rotation, expansion and the downsize of videos from all directions of the sub-images. Experimental results show that the proposed digital image stabilization algorithm can provide continuously stabilized videos and tracking object movements.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.4
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• pp.259-266
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• 2015

This study presents new scheme for various walking pattern of biped robot under the limitted enviroments. We show that the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multilayer backpropagation neural network identification is simulated to obtain a learning control solution of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The main advantage of our scheme is that we do not require any knowledge about the system dynamic and nonlinear characteristic, and can therefore treat the robot as a black box. It is also shown that the neural network is a powerful control theory for various trajectory tracking control of biped robot with same learning-vase. That is, we do net change the control parameter for various trajectory tracking control. Simulation and experimental result show that the neural network is practically feasible and realizable for iterative learning control of biped robot.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.269-277
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• 2008

This paper proposes a necessary and sufficient condition of convergence in the $L_2$-norm sense for a feedback-based iterative learning control (ILC) system including a multi-input multi-output (MIMO) linear time-invariant (LTI) plant. It is shown that the convergence conditions for a nominal plant and an uncertain plant are equal to the nominal performance condition and the robust performance condition in the feedback control theory, respectively. Moreover, no additional effort is required to design an iterative learning controller because the performance weighting matrix is used as an iterative learning controller. By proving that the least upper bound of the $L_2$-norm of the remaining tracking error is less than that of the initial tracking error, this paper shows that the iterative learning controller combined with the feedback controller is more effective to reduce the tracking error than only the feedback controller. The validity of the proposed method is verified through computer simulations.

• International Journal of Ocean System Engineering
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• v.1 no.3
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• pp.120-135
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• 2011

This paper proposes a model based trajectory tracking control scheme for under-actuated underwater robotic vehicles. The difficulty in stabilizing a non-linear system using smooth static state feedback law means that the design of a feedback controller for an under-actuated system is somewhat challenging. A necessary condition for the asymptotic stability of an under-actuated vehicle about a single equilibrium is that its gravitational field has nonzero elements corresponding to non-actuated dynamics. To overcome this condition, we propose a continuous time-varying control law based on the direct estimation of vehicle dynamic variables such as inertia, damping and Coriolis & centripetal terms. This can work satisfactorily under commonly encountered uncertainties such as an ocean current and parameter variations. The proposed control law cancels the non-linearities in the vehicle dynamics by introducing non-linear elements in the input side. Knowledge of the bounds on uncertain terms is not required and it is conceptually simple and easy to implement. The controller parameter values are designed using the Taguchi robust design approach and the control law is verified analytically to be robust under uncertainties, including external disturbances and current. A comparison of the controller performance with that of a linear proportional-integral-derivative (PID) controller and sliding mode controller are also provided.