• 대한기계학회논문집A
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• 제20권2호
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• pp.399-407
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• 1996

A fuzzy learning algorithm to get the optimal fuzzy rules is presented in this paper. The algorithm introduces a reference model to generate a desired output and a performance index funtion instead of the performance index table. The performance index funtion is a cost function based on the error and error-rate between the reference and plant output. The cost function is minimized by a gradient method and the control input is also updated. In this case, the control rules which generate the desired response can be obtained by changing the portion of the error-rate in the cost funtion. In SISO(Single-Input Single- Output)plant, only by the learning delay, it is possible to experss the plant model and to get the desired control rules. In the long run, this algorithm gives us the good control rules with a minimal amount of prior informaiton about the environment.

• 제어로봇시스템학회논문지
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• 제2권3호
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• pp.165-173
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• 1996

It is not desirable to have too large control input in control systems, because there are usually a limitation for the input magnitude and cost for the input energy. Previous papers in the iterative learning control did not considered on these points. In this paper, an iterative learning control method is proposed for a class of nonlinear systems with consideration on input magnitude by adopting a concept of cost function consisting of the output error and the input magnitude in quadratic form. We proposed a new input update law with an input penalty function. If we choose a reasonable input penalty function, the two control objectives, good command following and small input energy, can be achieved. The characteristics of the proposed method are shown in the simulation examples.

• 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.

• 전자공학회논문지B
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• 제33B권5호
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• pp.25-32
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• 1996

This paper will propose the direct adaptive learning control scheme based on adaptive control technique and intelligent control theory for a nonlinear system. Using the proposed learning control scheme, we will apply to on-line control an uncertain but for model perfect matching, it's structure condition is known. The effectiveness of the proposed control schem will be illustrated by simulations of a robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.230-235
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• 1992

We propose a method of generating data to train a neural network controller. The data can be prepared directly by an iterative learning technique which repeatedly adjusts the control input to improve the tracking quality of the desired trajectory. Instead of storing control input data in memory as in iterative learning control, the neural network stores the mapping between the control input and the desired output. We apply this concept to the trajectory control of a two link robot manipulator with a feedforward neural network controller and a feedback linear controller. Simulation results show good generalization of the neural network controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.364-367
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• 1995

In this paper, a design method of learning flight control system via input matching is proposed. The proposed learning control system is a simple structure which has an artificial neural network and feedback mechanism, and it is a useful method to control nonlinear systems.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.123-126
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• 1995

Characteristics of control system design using Universal Learning Network (U.L.N.) are that a system to be controlled and a controller are both constructed by U.L.N. and that the controller is best tuned through learning. U.L.N has the same generalization ability as N.N.. So the controller constructed by U.L.N. is able to control the system in a favorable way under the condition different from the condition of the control system in learning stage. But stability can not be realized sufficiently. In this paper, we propose a robust control method using U.L.N. and second order derivatives of U.L.N.. The proposed method can realize better performance and robustness than the commonly used Neural Network. Robust control considered here is defined as follows. Even though initial values of node outputs change from those in learning, the control system is able to reduce its influence to other node outputs and can control the system in a preferable way as in the case of no variation. In order to realize such robust control, a new term concerning the variation is added to a usual criterion function. And parameter variables are adjusted so as to minimize the above mentioned criterion function using the second order derivatives of criterion function with respect to the parameters. Finally it is shown that the controller constricted by the proposed method works in an effective way through a simulation study of a nonlinear crane system.

• 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.

• 수산해양교육연구
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• 제28권6호
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• pp.1834-1847
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• 2016

Main objectives of the this study are: to develop a model of "Flipped Leaning" that is designed to enhance self-directed learning, learning motivation and self-control, and to verify its effectiveness-in higher education. The verification process initially concentrated on the feasibility study of the model with a thorough literature review and case analyses; then, its general and practical applicability were tested with a field study. As a result, first, the CHANGE Class Model, specifically designed for effective and efficient "Flipped Learning", was developed. It is thus named for the stages that the learning process takes place in the model-i.e., (1) Check ${\rightarrow}$ (2) Ask ${\rightarrow}$ (3) Notice ${\rightarrow}$ (4) Group presentation ${\rightarrow}$ (5) Evaluation, and it emphasizes the dynamic, questions centered (i.e. back and forth between the students and the instructor as well as between the students) learning process. Second, the Model was instrumental in enhancing self-directed learning, learning motivation and self-control; thus, as a result, it significantly improved the effectiveness, the level of concentration and the attractiveness of the learning process. The value of this study lies in pointing to a clear plan to allow a student in higher learning to set-up a self-directed learning plan, to be able to control it while being continuously motivated to complete it.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.534-540
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• 1993

In this paper, we present an iterative learning method of compensating for position sensor error. The previously known compensation algrithms need a special perfect position sensor or a priori information about error sources, while ours does not. To our best knowledge, any iterative learning approach has not been taken for sensor error compensation. Furthermore, our iterative learning algorithm does not have the drawbacks of the existing iterative learning control theories. To be more specific, our algorithm learns a uncertain function inself rather than its special time-trajectory and does not request the derivatives of measurement signals. Moreover, it does not require the learning system to start with the same initial condition for all iterations. To illuminate the generality and practical use of our algorithm, we give the rigorous proof for its convergence and some experimental results.