• Journal of Institute of Control, Robotics and Systems
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• v.10 no.1
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• pp.42-46
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• 2004

A 2-mass resonant system is one that has a flexible coupling between a load and a driving motor. Due to this flexibility, the system often suffers vibration especially when the motor is controlled for higher speed command. In order to suppress such a vibration, an iterative learning control is applied to the 2-mass resonant system in this paper. The motor speed is controlled according to the relation with the load speed. The desired speed trajectories are derived under the condition for no vibration. The simulation result suggests that the proposed method effectively suppresses the vibration even when there exist model uncertainties.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.745-750
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• 2008

Uncertain dynamic parameters and joint flexibility have been problem to control robot manipulator precisely. Hence, even if the controller tracks the desired trajectory well with the feedback of the motor encoders, it is hard to achieve the desired behavior at the end-effector. In this paper, robot trajectory is taught by a general heuristic iterative learning control (ILC) algorithm in order to reduce tracking error of the tool center point (TCP) and the results of tracking with 6 DOF industrial robot manipulator are presented. The performance is verified based on ISO 9283.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.80-89
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• 2014

A stochastic optimal power flow (S-OPF) model considering uncertainties of load and wind power is developed based on chance constrained programming (CCP). The difficulties in solving the model are the nonlinearity and probabilistic constraints. In this paper, a limit relaxation approach and an iterative learning control (ILC) method are implemented to solve the S-OPF model indirectly. The limit relaxation approach narrows the solution space by introducing regulatory factors, according to the relationship between the constraint equations and the optimization variables. The regulatory factors are designed by ILC method to ensure the optimality of final solution under a predefined confidence level. The optimization algorithm for S-OPF is completed based on the combination of limit relaxation and ILC and tested on the IEEE 14-bus system.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.346-346
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• 2000

We proposed a method of second-order iterative learning control with feedback, which shows an enhancement of convergence speed and robustness to the disturbances in our previous study. In this paper, we show that the proposed second-order iterative learning control algorithm with feedback is more effective and has better convergence performance than the algorithm without feedback in the case of the existence of initial condition errors. And the convergence woof of the proposed algorithm in the case of the existence of initial condition error is given in detail, and the effectiveness of the Proposed algorithm is shown by simulation results.

• The Journal of Korea Robotics Society
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• v.16 no.1
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• pp.35-40
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• 2021

As the robot industry develops, industrial automation uses industrial robots in many parts of the manufacturing industry. However, rigidity-based conventional robots have a disadvantage in that they are challenging to use in environments where they grab fragile objects or interact with people because of their high rigidity. Therefore, researches on soft robot have been actively conducted. The soft robot can hold or manipulate fragile objects by using its compliance and has high safety even in an atypical environment with human interaction. However, these advantages are difficult to use in dynamic situations and control by the material's nonlinear behavior. However, for the soft robot to be used in the industry, control is essential. Therefore, in this paper, real-time PD control is applied, and the behavior of the soft actuator is analyzed by providing various waveforms as inputs. Also, Iterative learning control (ILC) is applied to reduce errors and select an ILC type suitable for soft actuators.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.5
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• pp.64-72
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• 2004

An extension of interaction matrix formulation to the problem of system and disturbance identification for a plant that is corrupted by both process and output disturbances is presented. The teaming control develops controllers that learn to improve their performance at executing a given task, based on experience performing this task. The simplest forms of loaming control are based on the same concept as integral control, but operating in the domain of the repetitions of the task. This paper studies the use of such controllers in a decentralized system, such as a robot moving on the vertical plane with the controller for each link acting independently. The basic result of the paper is to show that stability and iterative precision of the learning controllers for all subsystems when the coupling between subsystems is turned off, assures stability of the decentralized teaming in the coupled system, provided that the sample time in the digital teaming controller is sufficiently short. The methods of teaming system are shown up for the iterative precision of each link.

• Educational Technology International
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• v.19 no.2
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• pp.229-254
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• 2018

Universities in Korea have driven universities' new attempts to adopt more learner-centered and active learning in English. Problem-based Learning (PBL) is one of the well-known constructive teaching and learning methodologies in higher education. Our research goal was to design and develop the optimal PBL practices for a college physics course taught in English to promote learning and course satisfaction. For four semesters, we have tried and adjusted PBL components, and looked at the trend of the exam scores and group work achievement in each semester. We found that the number of problems and the duration of problem solving are the critical factors that influence the effect of PBL in a college physics course taught in English by going through iterative implementation. The iterative process of applying, designing, and constructing PBL to physics classes was meaningful not only in that we have found the optimal PBL model for learning a college physics course, but also in that we have been reflecting on the continuous interaction with learners during the course.

• Journal of the Korea society of information convergence
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• v.6 no.2
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• pp.15-22
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• 2013

Applying iterative learning utilizing SNS & Community to the class for pre-kindergarten teachers, the effectiveness of teaching satisfaction, self-efficacy, and curriculum understanding was verified. A iterative learning model utilizing SNS & Community in teachers leading traditional off-line teaching at college education field was applied separately into thinking to one-self by advance organizer, thinking together by presentation in the beginning of the class, and sharing the thoughts by community activities after the class. Iterative learning begins by being sent SNS to students from teachers before the class, but learners for themselves subsequently start to proceed self-directed learning activities. As a result, class satisfaction and understanding of pedagogy have been increased, and it had a positive influence on self-efficacy. Thus, it is to suggest utilizable SNS of professors and a teaching method utilizing Community to college students who need basic learning skills.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.2
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• pp.85-94
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• 2002

In this paper, we show that the 2nd-order iterative learning control algorithm with CITE is more effective and has better convergence performance than the algorithm without CITE in the case of the existence of initialization errors, for the trajectory-tracking control of dynamic systems with unidentified parameters. In contrast to other known methods, the proposed learning control scheme utilize more than one past error history contained in the trajectories generated at prior iterations, and a CITE term is added in the learning control scheme for the enhancement of convergence speed and robustness to disturbances and initialization errors. And the convergence proof of the proposed algorithm in the case of the existence of initialization error is given in detail, and the effectiveness of the proposed algorithm is shown by simulation results.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.148-157
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• 1996

Availability of input trajectories corresponding to desired output trajectories is often important in designing control systems for batch and other transient processes. In this paper, we propose a predictive control-type model-based iterative learning algorithm which is applicable to finding the nominal input trajectories of a linear time-invariant batch process. Unlike the other existing learning control algorithms, the proposed algorithm can be applied to nonsquare systems and has an ability to adjust noise sensitivity as well as convergence rate. A simple model identification technique with which performance of the proposed learning algorithm can be significantly enhanced is also proposed. Performance of the proposed learning algorithm is demonstrated through numerical simulations.