• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.1113-1119
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• 1989

Neural network control has many innovative potentials for intelligent adaptive control. Among many, it promises real time adaption, robustness, fault tolerance, and self-learning which can be achieved with little or no system models. In this paper, a dynamic robot controller has been developed based on a backpropagation neural network. It gradually learns the robot's dynamic properties through repetitive movements being initially trained with a PD controller. Its control performance has been tested on a simulated PUMA 560 demonstrating fast learning and convergence.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.50.4-50
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• 2001

In this paper, exploiting repetitive properties, a constrained digital regulation technique for first order hyperbolic PDE systems is proposed that guarantees the stability and performance of the closed loop system.

• 한국산업융합학회 논문집
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• 제18권4호
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• pp.224-230
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• 2015

In this paper, it is presented a learning controller for repetitive walking control of biped walking robot. We propose the iterative learning control algorithm which can learn periodic nonlinear load change ocuured due to the walking period through the intelligent control, not calculating the complex dynamics of walking robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of intelligent control to biped robotic motion is shown via dynamic simulation with 25-DOF biped walking robot.

• 한국국방경영분석학회지
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• 제36권2호
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• pp.1-10
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• 2010

Learning rate is generally applied to estimate an appropriate production labor cost. Learning effect is obtained from repetitive work during the production period under 3 assumptions ; homogeneous production, same producer, quantity measure in continuous unit. However, production breaks occur frequently in Korean defense industry environment because of budget constraint and annual requirements. In this case previous learning effect can not be applied due to learning loss. This paper proposed the application of learning rate when a production break occurs in Korea defense industry. To obtain a learning loss, we surveyed various learning loss factors for different production breaks(6, 12, 18 months) from 4 defense industry companies. Then, we estimate the first unit labor hours in re-production phase after production break using Anderlohr method and Retrograde method with the result of the survey. This work is the first attempt to show a method which defines and evaluates the learning loss factors in Korean defense industry environment.

• 정보처리학회논문지A
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• 제9A권1호
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• pp.105-112
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• 2002

본 논문은 지리학습의 시각 및 공간의 학습효과를 향상시키기 위해 지리정보시스템 기반의 코스웨어를 개발하는데 목적을 둔다. 기존의 코스웨어는 학습자에게 단순히 텍스트나 이미지와 같은 시각적인 정보만을 제공하기 위해 학습자의 학습의욕을 제어할 수 있도록 했다. 이러한 코스웨어를 본 논문에서는 지리학습 시스템으로 정의한다. 본 지리학습시스템은 학습평가 후에 이루어지는 피드백을 통해 완전학습과 반복학습이 가능하다. 또한 학습자는 구현한 지리학습 응용모듈을 이용하여 직접적인 학습참여와 웹사이트에서의 정보검색이 가능하다.

• International Journal of Precision Engineering and Manufacturing
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• 제7권1호
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• pp.62-66
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the learning control field was learning in robots doing repetitive tasks such as an assembly line works. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown for the iterative precision of each link.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 1996년도 추계학술발표회 발표논문집
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• pp.217-227
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• 1996

The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performin this specific task. In a previous work［6］, the authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification ad control. This paper develops improved indirect learning control algorithms, and studies the use of such controllers in decentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The basic result of the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample time in the digital learning controller is sufficiently short.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 1996년도 추계 학술 발표회 발표논문집
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• pp.217-227
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• 1996

The new filed of learning control develops controllers that learn to improve their performance at executing a given task , based on experience performing this specific task. In a previous work[6], authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper develops improved indirect learning control algorithms, and studies the use of such controller indecentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an asssembly line. This paper starts with decentralized discrete time systems. and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The resultof the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample tie in the digital learning controller is sufficiently short.

• 로봇학회논문지
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• 제17권1호
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• pp.1-7
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• 2022

Robots are widely used in industries and services. Traditional robots have been used to perform repetitive tasks in a fixed environment, and it is very difficult to solve a problem in which the physical interaction of the surrounding environment or other objects is complicated with the existing control method. Reinforcement learning has been actively studied as a method of machine learning to solve such problems, and provides answers to problems that robots have not solved in the conventional way. Studies on the learning of all physical robots are commonly affected by noise. Complex noises, such as control errors of robots, limitations in performance of measurement equipment, and complexity of physical interactions with surrounding environments and objects, can act as factors that degrade learning. A learning method that works well in a virtual environment may not very effective in a real robot. Therefore, this paper proposes a weighted sum method and a linear regression method as an effective and accurate learning method in a noisy environment. In addition, the bottle flipping was trained on a robot and compared with the existing learning method, the validity of the proposed method was verified.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.99-102
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• 1996

A novel iterative learning control scheme comprising a unique feedforward learning controller and a disturbance observer is proposed. Disturbance observer compensates disturbance due to parameter variations, mechanical nonlinearities, unmodeled dynamics and external disturbances. The convergence and robustness of the proposed controller is proved by the method based on Lyapunov stability theorem. The results of numerical simulation are shown to verify the effectiveness of the proposed control scheme.