• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.197-200
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• 1996

This paper considers feedback error learning neural networks for robot manipulator control. Feedback error learning proposed by Kawato [2,3,5] is a useful learning control scheme, if nonlinear subsystems (or basis functions) consisting of the robot dynamic equation are known exactly. However, in practice, unmodeled uncertainties and disturbances deteriorate the control performance. Hence, we presents a robust feedback error learning scheme which add robustifying control signal to overcome such effects. After the learning rule is derived, the stability is analyzed using Lyapunov method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1911-1916
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• 2005

In this study, the basic motor control system had been investigated. The Discrete-Time Feedback Error Learning (DTFEL) method is used to control this system. This method is anologous to the original continuous-time version Feedback Error Learning(FEL) control which is proposed as a control model of cerebellum in the field of computational neuroscience. The DTFEL controller consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such the tracking perfect, the adaptive law is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The PD control theory is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1981-1986
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• 2004

In this study, the basic motor control system had been investigated. The controller for this study consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such a tracking perfect, an adaptive law based on Feedback Error Learning (FEL) is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The theory in $H^{\infty}$-Control is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.699-701
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• 2004

This paper proposes the algorithm of servo- writing based on feedback error learning neural networks. The controller consists of feedback controller using PID and feedforward controller using gaussian radial basis function network. Because the RBFNs are trained by on-line rule, the controller has adaptation capability. The performance of the proposed controller is compared to that of conventional PID controller. Proposed algorithm shows better performance than PID controller.

• Physical Therapy Korea
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• v.7 no.1
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• pp.55-63
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• 2000

Physical therapists have been using balance and weight shifting training to induce improvements in standing and walking. This study compared the effects of kinetic feedback frequency and concurrent kinetic feedback on the performance and learning of a weight shifting skill in young, nondisabled adults. Sixteen young adults without known impairment of the neuromusculoskeletal system volunteered for the study. Subjects in each of three kinetic feedback groups performed a weight shifting task in an attempt to minimize error between their effort and a center of pressure (COP) template for a 12 second period. Feedback was provided: 1) concurrently (concurrent feedback), 2) after each trial (100% feedback), 3) after every other trial (50% feedback). Immediate and delayed (24 hour) retention tests were performed without feedback. During acquisition phase, the concurrent feedback group exhibited less error than either of the post response feedback group. For the immediate retention test, the 50% feedback group exhibited less error than did the 100% feedback and concurrent feedback. During the delayed retention, 50% feedback group displayed less error than did the other groups. But no significant differences were found between groups. These results suggest that practice with concurrent feedback is beneficial for the immediate performance, but not for the learning of this weight shifting skill. Lower frequency of feedback resulted in more permanent changes in the subject's ability to complete the task.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.269-277
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• 2008

This paper proposes a necessary and sufficient condition of convergence in the $L_2$-norm sense for a feedback-based iterative learning control (ILC) system including a multi-input multi-output (MIMO) linear time-invariant (LTI) plant. It is shown that the convergence conditions for a nominal plant and an uncertain plant are equal to the nominal performance condition and the robust performance condition in the feedback control theory, respectively. Moreover, no additional effort is required to design an iterative learning controller because the performance weighting matrix is used as an iterative learning controller. By proving that the least upper bound of the $L_2$-norm of the remaining tracking error is less than that of the initial tracking error, this paper shows that the iterative learning controller combined with the feedback controller is more effective to reduce the tracking error than only the feedback controller. The validity of the proposed method is verified through computer simulations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.466-471
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• 1994

To make a dynamic system a given desired motion trajectory, a new feedback error learning scheme is proposed which is based on the repeatability of dynamic system motion. This method is composed of feedforward and feedback control laws. A benefit of this control scheme is that the input pattern that generates the desired motion can be formed without estimating the physical parameters of system dynamics. The numerical simulations show the good performance of the proposed scheme

• English Language & Literature Teaching
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• v.13 no.1
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• pp.111-131
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• 2007

This study explores teacher's corrective feedback types in an error treatment sequence in Korean EFL classroom setting. Corrective feedback moves are coded as explicit correction, recast, or initiations to self-repair. The frequency and distribution of each corrective feedback type are examined. But the special focus was given on feedback types eliciting learner's self-repair (clarification request, metalinguistic feedback, elicitation, and repetition of error) because initiations to self-repair are believed to facilitate language learning more than other strategies. The results of the study are as follows. First, there was an overwhelming tendency for teacher to use recasts whereas initiations to self-repair were not used as much as recast (52.4% vs. 29.5%). Second, the teacher tended to select feedback types in accordance with error types: namely, recasts after phonological, lexical, and translation errors and initiations to self-repair after grammatical errors though the differences were not significant. Finally, teacher's belief and students' expectation on corrective feedback were compared with actual corrective feedback representations respectively and some mismatches were found. Though both teacher and the students acknowledged the importance and necessity of self-repair, self-repair were not put into practice as such. Therefore, this study suggests more initiations to self-repair be used for effective language learning.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.345-350
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• 1993

This paper presents an on-line feedback error learning control algorithm for a magnetic levitation system. It will be shown that even in the case of abrupt changes of the system parameters and disturbanes, the control performance is still very satisfactory.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.330-333
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• 2005

In this study, a method of designing a neurointerface using neural network (NN) is proposed for controlling nonholonomic mobile robots. According to the concept of virtual master-slave robots, in particular, a partially stable inverse dynamic model of the master robot is acquired online through the NN by applying a feedback-error learning method, in which the feedback controller is assumed to be based on a PD compensator for such a nonholonomic robot. A tracking control problem is demonstrated by some simulations for a nonholonomic mobile robot with two-independent driving wheels.