• 전기학회논문지P
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• 제59권1호
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• pp.29-34
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• 2010

This paper presents an intelligent learning controller for repetitive walking motion of biped walking robot. The proposed learning controller consists of an iterative learning controller and a direct learning controller. In the iterative learning controller, the PID feedback controller takes part in stabilizing the learning control system while the feedforward learning controller plays a role in compensating for the nonlinearity of uncertain biped walking robot. In the direct learning controller, the desired learning input for new joint trajectories with different time scales from the learned ones is generated directly based on the previous learned input profiles obtained from the iterative learning process. The effectiveness and tracking performance of the proposed learning controller to biped robotic motion is shown by mathematical analysis and computer simulation with 12 DOF biped walking robot.

• 대한기계학회논문집A
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• 제21권11호
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• pp.1819-1826
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• 1997

Direct-drive robots are suitable to the position and force control with high accuracy, but it is difficult to design a controller because of the system's nonlinearity and link-interactions. This paper is concerned with the study of the force control of direct-drive robots. The proposed algorithm consists of feedback controllers and a neural network. After the completion of learning, the output of feedback controller is nearly equal to zero, and the neural network controller plays an important role in the control system. Therefore, the optimum retuning of parameters of feedback controllers is unnecessary. In other words, the proposed algorithm does not require any knowledge of the controlled system in advance. The effectiveness of the proposed algorithm is demonstrated by the experiment on the force control of the parallelogram link-type direct-drive robot.

• 지식경영연구
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• 제10권4호
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• pp.31-42
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• 2009

With the rapid development of information technology, e-learning is growing in corporate. However, there are still problems in learning, such as low learning persistence rate. Learning outcomes are complex phenomenon influenced by a multitude of factors, it is need to considering the direct and indirect causal relationship among various factors. Therefore, the purpose of this study was to develop the causal model that explains the learning outcomes (satisfaction learning persistence) in corporate e-learning. This study was also intended to examine the causal relationship between the interaction and learning persistence through satisfaction mediators. For this, online survey was conducted with a sample of 270 learners who enrolled in cyber training course at A company. The major findings of this study are as follows: First, internality (internal locus of control, ${\beta}=.154$), interaction (${\beta}=.489$), satisfaction (${\beta}=.304$) have direct effect on learning persistence. Second, the interaction has direct effect on the satisfaction (${\beta}=.320$). Third, the satisfaction has direct effect on the learning persistence, and mediating the interaction and learning persistence. This result will contribute to build a learning strategy to improve learning outcomes.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.694-696
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• 2000

In this paper, the Direct Learning Control is applied to robot's trajectory tracking control to solve the problem that lies in the existing Iterative Learning Control(ILC) and the tracking Performance is analyzed and the better approach is searched using computer simulation and experiments. It is assumed that the Direct Learning Control(DLC) is saved onto memory basically after obtaining control input Profiles for several Periodic output trajectories using the ILC. In case the new output trajectory has special relations with the previous output trajectories, there is an advantage that the desired control input profile can be obtained without iterative executions only using the DLC. The robot's tracking control system is comprised of DSP chip. A/D converter, D/A converter and high-speed pulse counter included in the control board and the performance is examined by carrying out the tracking control for the given output trajectory.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권2호
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• pp.76-80
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• 2005

In this paper, a Direct Learning Control (DLC) method is proposed for linear feedback systems to improve the tracking performance when the task of the control system is repetitive. DLC can generate the desired control input directly from the previously learned control inputs corresponding to other output trajectories. It is assumed that all the desired output functions given to the system have some relations called proportionality and it is shown by mathematical analysis that DLC can be utilized to genera additional control efforts for the perfect tracking. To show the validity and tracking performance of the proposed method, some simulations are performed for the tracking control of a linear system with a PI controller.

• 전기학회논문지
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• 제56권3호
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• pp.611-614
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• 2007

In this paper, a Direct Learning Control (DLC) method is presented to generate a virtual reference input for linear feedback systems to improve the output tracking performance. The original reference input is effectively modified by the DLC without any iterative learning process. The presented DLC is designed based on the information on the relative degree of a system and previously generated virtual reference inputs. It is illustrated by simulations that the virtual reference input generated by the proposed DLC can achieve high tracking performance, although the reference input cannot be appropriately shaped by using existing DLC methods.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1054-1057
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• 1993

A direct method of fuzzy inference and a fuzzy algorithm with learning function are applied to the steam generator level control of nuclear power plant. The fuzzy controller by use of direct inference can control the steam generator in the entire range of power level. There is a little long response time of fuzzy direct inference controller at low power level. The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0%∼30% of full power). Response time of steam generator level control at low power level with this rule base is shown generator level control at low power level with this rule base is shown to be shorter than that of fuzzy controller with direct inference.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.369-372
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• 1993

In this thesis, we consider an iterative learning controller to control the continuous trajectory of 2 links direct drive robot manipulator and process computer simulation and real-time experiment. To improve control performance, we adapt an iterative learning control algorithm, drive a sufficient condition for convergence from which is drived extended conventional control algorithm and get better performance by extended learning control algorithm than that by conventional algorithm from simulation results. Also, experimental results show that better performance is taken by extended learning algorithm.

• 대한기계학회논문집A
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• 제24권3호
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• pp.653-660
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• 2000

The automatization by industrial robot of today is merely rely on to the simple position repeating works, but requirements of research and development to the force control which would adapt positively to various restriction or contacting works to environment. In this paper, a learning control algorithm using, neural networks is proposed for the position and force control by a direct-drive robot. The proposed controller is the feedback controller to which the learning function of neural network is added on to and has a character of improving controller's efficiency by learning. The effectiveness of the proposed algorithm is demonstrated by the experiment on the hybrid position and force control of a parallelogram link robot with a force sensor.

• 전자공학회논문지SC
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• 제40권2호
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• pp.19-25
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• 2003

본 논문에서는 주어진 작업을 반복적으로 수행하는 다입력 다출력 비선형시스템에 대하여 시스템의 (벡터)상대차수 개념을 이용한 확장된 형태의 직접학습제어를 제안한다. 기존의 직접학습제어가 적용될 수 있는 시스템은 상대차수가 제한적인 시스템임을 보이고 고차의 상대차수를 갖는 시스템에 적용 가능한 제어 법칙을 제시한다. 이 제어법칙을 이용하여 다른 형태의 출력 궤적들에 대한 학습을 통하여 얻어진 제어입력들로부터 새로 주어진 원하는 출력 궤적에 대응하는 제어입력을 직접적으로 생성한다. 제안된 직접학습제어의 타당성 및 성능을 보이기 위하여 2축 스카라 로봇에 대한 궤적추종제어의 시뮬레이션 결과를 제시한다