• 한국지능시스템학회논문지
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• 제14권6호
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• pp.693-698
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• 2004

2관성 공진계는 전동기와 부하 사이에 탄성이 있는 동력 전달 체계를 포함하는 시스템으로 고속 제어시 진동이 발생된다. 본 논문에서는 반복 학습 제어를 이용하여 이와 같은 2관성 공진계의 위치 제어에 대한 진동 억제 기법을 제안한다. 제안된 기법은 측정하기 어려운 부하에 대해 진동이 발생하지 않는 속도궤적을 산출하고 이에 해당하는 전동기 속도 및 위치 궤적에 대해 반복 학습 제어기법을 적용하는 방식으로 구성되어 있다. 또한 초기 위치 오차에 의해 발생되는 진동을 억제하기 위한 방법도 제시된다. 제안된 방법은 2 관성 공진계에 대한 모델링이 정확하지 않더라도 진동 없이 정확한 위치 제어가 가능하다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2390-2392
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• 2003

ILC(Iterative Learning Control: 이하 ILC)는 현재 기계, 전기, 화학 등 많은 분야에 널리 적용되고 있다. ILC는 특히 반복적인 trajectory tracking Control 문제에 아주 효과적인 방법 중의 하나이다. 하지만 ILC는 메모리 기반의 scheme로서 trajectory tracking을 위해서는 많은 메모리를 요구하게 된다. 한편, 자세한 관찰에 의하면 인간의 팔, 다리 등의 관절의 움직임은 아주 정확하지가 않다. 이러한 사실로 미루어 인간이 정화한 모션을 취하는데 드는 비용을 줄이고자 모션 명령을 간단히 한다는 가정을 추론 해 낼 수 있다. 이러한 가정에 기초하여 우리는 ILC 명령을 간단히 하기 위해서 약간의 trajectory tracking의 정확성을 회생하는 메커니즘을 제안한다. 간단해진 ILC 명령은 적은 메모리 공간에 저장될 것이다. 또한, 로봇의 trajectory tracking을 위한 기존의 방법들은 아주 복잡할 뿐만 아니라 하나의 task의 수행만이 가능할 뿐 어떤 일반화의 방법도 제시하지 못하고 있다. 그래서 본 논문에서는 ILC 명령의 scaling에 대한 메커니즘을 제공하여 하나의 trajectory에 대해서 비슷한 모양이지만 다른 크기와 속도를 가지는 trajectory를 구현 할 수 있도록 하였다.

• 제어로봇시스템학회논문지
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• 제9권6호
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• pp.435-441
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• 2003

This paper addresses that an approximation and tracking control of realtime recurrent neural networks(RTRN) using two-dimensional iterative teaming algorithm for an electro-hydraulic servo system. Two dimensional learning rule is driven in the discrete system which consists of nonlinear output fuction and linear input. In order to control the trajectory of position, two RTRN with the same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm are able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two identical RTRN was very effective to trajectory tracking of the electro-hydraulic servo system.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
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• pp.244-248
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• 1996

Nowadays, most shop floors using industrial robots have many problems such as constructing robot workcell, generating robot arm moving trajectory, etc.. In the case of programming robot-arms for a specific task, shop operator commonly use the teach pendant to record the target position and determine the moving trajectory. However, such a teaching process may result in an inefficient trajectory in the sense of moving distance and joint angle fluctuation. Moreover, shop operators who have little knowledge about robot programming process need a lot of learning time and cost. The purpose of this paper is to propose a user friendly robot programming method to program robot-arms easily and efficiently for shop operator so that the programming time is reduced and a short and stable trajectory is obtained.

• Journal of Advanced Marine Engineering and Technology
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• 제38권10호
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• pp.1281-1286
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• 2014

This paper presents an iterative learning control (ILC) approach for tracking problems with specified data points that are desired points at certain time instants. To design ILC systems for such problems, unlike traditional ILC approaches, an algorithm which updates not only the control signal but also the reference trajectory at each trial will be developed. The relationship between the reference trajectory and ILC control in tracking problems where there are specified data points through which the system should pass is investigated as the rate of convergence. In traditional ILC, the desired data is stored in a tracking profile file. Due to the huge size of the data file containing the target points, it is important to reduce the computational cost. Finally, simulation results of the presented technique are mentioned and compared to other related works to confirm the effectiveness of proposed scheme.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.969-981
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• 2018

Recent advances in machine learning approaches such as deep neural network and reinforcement learning offer significant performance improvements in generating detailed and varied motions in physically simulated virtual environments. The optimization methods are highly attractive because it allows for less understanding of underlying physics or mechanisms even for high-dimensional subtle control problems. In this paper, we propose an efficient learning method for stochastic policy represented as deep neural networks so that agent can generate various energetic motions adaptively to the changes of tasks and states without losing interactivity and robustness. This strategy could be realized by our novel trajectory search method motivated by the trust region policy optimization method. Our value-based trajectory smoothing technique finds stably learnable trajectories without consulting neural network responses directly. This policy is set as a trust region of the artificial neural network, so that it can learn the desired motion quickly.

• 한국산업융합학회 논문집
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• 제18권4호
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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.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권9호
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• pp.369-374
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• 2014

무인 자율주행 차량에서의 경로 생성 기법은 차량이 자동적으로 안전하고 효율적인 경로를 생성하고 주행할 수 있도록 해 준다. 경로에는 크게 전역경로와 지역경로가 있다. 전역경로는 차량이 출발점으로부터 도착점까지 가기 위해 주행해야 하는 구간을, 지역경로는 전역경로에서 얻은 구간을 주행하기 위해서 차량이 실제로 주행해야 할 경로를 의미한다. 본 논문에서는 지역경로 생성을 위하여 효율성 높은 곡선 함수를 사용하는 기존연구에서 더 나아가 학습을 통해 경로를 생성하는 방법을 제안한다. 먼저 강화학습을 통해서 후보경로에 대한 예측 보상 값을 얻고 보상 값이 최고가 되는 경로를 찾는 작업을 한다. 또한 인공 신경망을 통해서는 생성된 경로에 최적화된 조향 명령을 주기 위해 조향 각을 학습하는 작업을 한다. 더 나아가 주행하는 경로에 장애물이 발견되더라도 이를 효율적으로 회피하는 최적의 경로를 학습 기법을 통해 만들어낸다. 본 논문에서 제안된 알고리즘의 우수성은 실제 주행 환경으로 모델링한 시뮬레이션 실험을 통해 검증되었다.

• Journal of Multimedia Information System
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• 제5권3호
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• pp.195-200
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• 2018

Recently, as the sensor and big data analysis technology have been developed, there have been a lot of researches that analyze the purchase-related data such as the trajectory information and the stay time. Such purchase-related data is usefully used for the purchase pattern prediction and the purchase time prediction. Because it is difficult to find periodic patterns in large-scale human data, it is necessary to look at actual data sets, find various feature patterns, and then apply a machine learning algorithm appropriate to the pattern and purpose. Although existing papers have been used to analyze data using various machine learning methods, there is a lack of statistical analysis such as finding feature patterns before applying the machine learning algorithm. Therefore, we analyze the purchasing data of Songjeong Maeil Market, which is a data gathering place, and finds some characteristic patterns through statistical data analysis. Based on the results of 1, we derive meaningful conclusions by applying the machine learning algorithm and present future research directions. Through the data analysis, it was confirmed that the number of visits was different according to the regional characteristics around Songjeong Maeil Market, and the distribution of time spent by consumers could be grasped.

• Journal of Advanced Marine Engineering and Technology
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• 제38권10호
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• pp.1303-1309
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• 2014

In this paper, we present an iterative learning control (ILC) framework for tracking problems with predefined data points that are desired points at certain time instants. To design ILC systems for such problems, a new ILC scheme is proposed to produce output curves that pass close to the desired points. Unlike traditional ILC approaches, an algorithm will be developed in which the control signals are generated by solving an optimal ILC problem with respect to the desired sampling points. In another word, it is a direct approach for the multiple points tracking ILC control problem where we do not need to divide the tracking problem into two steps separately as trajectory planning and ILC controller.The strength of the proposed formulation is the methodology to obtain a control signal through learning law only considering the given data points and dynamic system, instead of following the direction of tracking a prior identified trajectory. The key advantage of the proposed approach is to significantly reduce the computational cost. Finally, simulation results will be introduced to confirm the effectiveness of proposed scheme.