• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.431-431
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• 2000

The modeling of 5-bar linkage robot manipulator dynamics by means of a mathematical and neural architecture is presented. Such a model is applicable to the design of a feedforward controller or adjustment of controller parameters. The inverse model consists of two parts: a mathematical part and a compensation part. In the mathematical part, the subsystems of a 5-bar linkage robot manipulator are constructed by applying Kawato's Feedback-Error-Learning method, and trained by given training data. In the compensation part, MLP backpropagation algorithm is used to compensate the unmodeled dynamics. The forward model is realized from the inverse model using the inverse of inertia matrix and the compensation torque is decoupled in the input torque of the forward model. This scheme can use tile mathematical knowledge of the robot manipulator and analogize the robot characteristics. It is shown that the model is reasonable to be used for design and initial gain tuning of a controller.

• 전자공학회논문지B
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• 제30B권4호
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• pp.67-75
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• 1993

The Fourier series are employed to approximate the input/output(I/O) characteristics of a dynamic system and, based on the approximation, a new learing control algorithm is proposed in order to find iteratively the control input for tracking a desired trajectory. The use of the Fourier approximation of I/O renders at least a couple of useful consequences: the frequency characteristics of the system can be used in the controller design and the reconstruction of the system states is not required. The convergence condition of the proposed algorithm is provided and the existence and uniqueness of the desired control input is discussed. The effectiveness of the proposed algorithm is illustrated by computer simulation for a robot trajectory tracking. It is shown that, by adding feedback term in learning control algorithm, robustness and convergence speed can be improved.

• 실천공학교육논문지
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• 제16권1_spc호
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• pp.11-19
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• 2024

Web programming courses are often included in university Computer Science programs as introductory and foundational computer programming courses. However, amateur programmers often have difficulty learning how to integrate HTML, CSS, JavaScript, and various preprocessors or libraries to create websites. Additionally, many web programming mistakes do not produce visible output in the browser. Therefore, in recent years, Front-End Code Playground (FECP) tools that incorporate HTML, CSS, and JavaScript into a single, online web-based application have become popular. These tools allow web coding to happen directly in the browser and provide immediate visual feedback to users. Such immediate visual feedback can be particularly beneficial for amateur coders to learn and practice with. Therefore, this study gathers data on various FECP tools, compares their differences, and provides an analysis of how such tools benefit students. This study concludes with an outline of the application of FECP to web programming courses to enhance the learning experience.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국제학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.999-1005
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• 1988

An iterative learning control scheme is presented with the aid of CMAC module. By enforcing the role of linear controller with the introduction of velocity feedback, it becomes possible to make the trajectory error equation stable. One advantage of this control scheme is that it does not require acceleration feedback. Computer simulation results shows a good performance of the scheme even in the case where the gravity is not compensated.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 1998년도 가을 학술발표논문집 Vol.25 No.2 (2)
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• pp.129-131
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• 1998

웹 에이전트는 사용자가 좀 더 손쉽게 웹 상의 정보를 얻을 수 있게 하는 것을 목표로 하는 인터넷 정보 검색 도구이다. 본 논문에서는 개인용 웹 에이전트 시스템에서 적합성 피드백(Relevance Feedback)에 의해 사용자의 취향을 학습하는 방법을 제시하고, 실험을 통하여 제시된 적합성 피드백에 의한 학습 방법이 사용자의 취향을 성공적으로 학습함을 보였다. 적합성 피드백을 혼용할 때와 각각 한가지만 사용할 때로 나누어 실험하였으며, 피드백이 진행되면서 검색결과의 정확도가 변화하는 정도를 관찰하였다.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2003년도 하계학술대회 논문집
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• pp.173-176
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• 2003

In this paper, a DLC method is suggested for linear feedback systems to improve the tracking performance when the task of the system is repetitive. DLC can generate the desired control input directly from the previously teamed control inputs corresponding to other output trajectories. It is assumed that all outputs considered in this paper have some relations called "proportionality. " To show the validity and tracking performance of the proposed method, some simulation are performed for the tracking control of a linear system with a PI controller.

• 한국정밀공학회지
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• 제29권1호
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• pp.79-86
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• 2012

This paper proposes PID and RIC (Robust Internal-loop Compensator) based motion controller using dual learning algorithm for position control of linear synchronous motor respectively. Its gains are auto-tuned by using two learning algorithms, reinforcement learning and neural network. The feedback controller gains are tuned by reinforcement learning, and then the feedforward controller gains are tuned by neural network. Experiments prove the validity of dual learning algorithm. The RIC controller has better performance than does the PID-feedforward controller in reducing tracking error and disturbance rejection. Neural network shows its ability to decrease tracking error and to reject disturbance in the stop range of the target position and home.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.85.4-85
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• 2001

A learning controller is presented for repetitive walking motion of biped robot. The learning control scheme learns the approximate inverse dynamics input of biped walking robot and uses the learned input pattern to generate an input profile of different walking motion from that learnt. In the learning controller, the PID feedback controller takes part in stabilizing the transient response of robot dynamics while the feedforward learning controller plays a role in computing the desired actuator torques for feedforward nonlinear dynamics compensation in steady state. It is shown that all the error signals in the learning control system are bounded and the robot motion trajectory converges to the desired one asymptotically. The proposed learning control scheme is ...

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.30-35
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• 1990

Learning control of a robot manipulator is proposed using the backpropagation neural network. The learning controller is composed of both a linear feedback controller and a neural network-based feedforward controller. The stability analysis of the learning controller is presented. Three energy functions are selected in teaching the neural network controller : 1/2.SIGMA.vertical bar torque error vertical bar $^{2}$, 1/2.SIGMA..alpha. vertical bar position error vertical bar $^{2}$ + .betha. vertical bar velocity error vertical bar $^{2}$ + .gamma. vertical bar acceleration error vertical bar $^{2}$ and learning methods are presented. Simulation results show that the learning controller which is learned to minimize the third energy function performs better than the others in tracking problems. Some properties of the learning controller are discussed with simulation results.

• 디지털융복합연구
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• 제5권2호
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• pp.123-134
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• 2007

The purpose of this study is to design and implementation of project learning site by using XML. The development of the Internet site for project learning was planned as per preparation, development and test/application stages. The research shows that the elements used for the development of the Internet site for project learning are to give learners motivation, specification of learning goals, reminiscence of preceding knowledge, positive participation in teaching activities, learning-guide feedback, evaluation, reinforcement and correction. It is expected that many teachers apply this model to their classes and show realistic results to motivate their students.