• International Journal of Safety
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• v.1 no.1
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• pp.1-6
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• 2002

It is important to control the high accurate position and force to prevent unexpected accidents by a robot manipulator. Direct-drive robots are suitable to the position and force control with high accuracy, but it is difficult to design a controller because of the system's nonlinearity and link-interactions. This paper is concerned with the study of the stabilization force control of direct-drive robots. The proposed algorithm is consists of the feedback controllers and the neural networks. After the completion of learning, the outputs of feedback controllers are nearly equal to zero, and the neural networks play an important role in the control system. Therefore, the optimum adjustment of control parameters is unnecessary. In other words, the proposed algorithm does not need any knowledge of the controlled system in advance. The effectiveness of the proposed algorithm is demonstrated by the experiment on the force control of a parallelogram link-type robot.

• Journal of Engineering Education Research
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• v.16 no.6
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• pp.3-10
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• 2013

The purpose of this study is to design the instructional model to develop logical thinking competency of engineering students and to investigate the effect of the model. The instructional model is composed of the virtual context (films were provided), problem solving, feedback, another problem solving with different perspectives, feedback. The process is looped. The results showed statistically significant improvements between pre- and post-test. The first standardized test of critical thinking showed the improvement from pre-test to post-test (d=0.646). The second test of logical thinking showed the improvement from pre-test to mid-term test (d=0.753) and improvement from mid-term to post-test (d=1.529).

• English Language & Literature Teaching
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• v.16 no.4
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• pp.1-18
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• 2010

Recasts, which are defined as implicit types of corrective feedback, have been the focus of numerous SLA researchers for more than a decade. A range of classroom-based observational and experimental research studies have explored how and when language teachers provide recasts to learners' ill-formed utterances and aimed to understand the role of recasts in language acquisition or learning. On the basis of previous studies on recasts, our study investigated when an ESL teacher provided recasts and how recasts were provided in his class. The research questions were as follows: (1) When does an ESL teacher provide recasts? (2) How does the teacher provide recasts? The data came from observations of one ESL classroom as well as consecutive-semi structured interviews with the teacher. The data analysis included transcriptions of teacher-student interactions in the target setting and categories of recasts according to the linguistic phenomena, which prompted recasting. Based on the findings, practical suggestions for ESL teachers were provided. [156 words].

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.987-994
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• 2000

A Linear Pulse Motor (LPM) is a direct drive motor that has good performance in terms of accuracy, velocity and acceleration compared to the conventional rotating system with toothed belts and ball screws. However, since an LPM needs supporting devices which maintain constant air-gap and has strong nonlinearity caused by leakage magnetic flux, friction and cogging, etc., there are many difficulties in improvement on accuracy with conventional control theory. Moreover, when designing the position controller of LPM, the modeling error and load variations has not been considered. In order to compensate these components, the neural network with conventional feedback controller is introduced. This neural network of feedback error learning type changes the current commands to improve position accuracy. As a result of experiments, we observes that more accurate position control is possible compared to conventional controller.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.140-148
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• 1996

This paper proposes a combined feedforward/feedback controller which uses jacobians of neural network. The jacobians are calculated form the neural network that identifies the nonlinear plant, which are used for designing a jacobian controller and for training a neural network controller. Normally, it takes much time to train the neural network controller. Combining the neural and the jacobian controller, it can be a stable controller from the beginning of training phase of neural network, and it can be implemented as a learning-while-functioning controller. Simulated resutls for the proposed controller show its effectiveness and better performances.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.3
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• pp.284-292
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• 1997

This paper is concerned with position control of direct drive robots. The proposed algorithm consists of the feedback controller and neural networks. Mter the completion of learning, the output of the feedback controller is nearly equal to zero, and the neural networks play an important role in the control system. Therefore, the optimum retuning of control parameters is unnecessary. In other words, the proposed algorithm does not need any knowledge of the con¬trolled system in advance. The effectiveness of the proposed algorithm is demonstrated by the experiment on the position control of a parallelogram link-type direct drive robot.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1012-1017
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• 2003

The development of a fast, accurate, and inexpensive position-controlled pneumatic actuator that may be applied to a variety of practical positioning applications with various external loads is described in this paper. A novel modified pulso width modulation (MPWM) valve pulsing algorithm allows on/off solenoid valves to be used in place of costly servo valves. A comparison between the system response of standard PWM technique and that of the novel modified PWM technique shows that the control performance is significantly increased. A state feedback controller with position, velocity and acceleration feedback is successfully implemented as the continuous controller. Switching algorithm of control parameter using learning vector quantization neural network (LVQNN) is newly proposed. which estimates the external loads of the pneumatic actuator. The effectiveness of the proposed control algorithms are demonstrated through experiments with various loads.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.1-6
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• 1992

A principle of "orthogonalization" is proposed as an extended notion of hybrid (force and position) control for robot manipulators under geometric endpoint constraints. The principle realizes the hybrid control in a strict sense by letting position and velocity feedback signals be orthogonal in joint space to the contact force vector whose components are exerted at corresponding joints. This orthogonalization is executed via a projection matrix computed in real-time from a gradient of the equation of the surface in joint coordinates and hence both projected position and velocity feedback signals become perpendicular to the force vector that is normal to the surface at the contact point in joint space. To show the important role of the principle in control of robot manipulators, three basic problems are analyzed, the first is a hybrid trajectory tracking problem by means of a "modified hybrid computed torque method", the second is a model-based adaptive control problem for robot manipulators under geometric endpoint constraints, and the third is an iterative learning control problem. It is shown that the passivity of residual error dynamics of robots follows from the orthogonalization principle and it plays a crucial role in convergence properties of both positional and force error signals.force error signals.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.8
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• pp.86-93
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• 2003

The development of a fast, accurate, and inexpensive position-controlled pneumatic actuator that may be applied to a variety of practical positioning applications with various external loads is described in this paper. A novel modified pulse width modulation (MPWM) valve pulsing algorithm allows on/off solenoid valves to be used in place of costly servo valves. A comparison between the system response of standard PWM technique and that of the novel modified PWM technique shows that the control performance is significantly increased. A state feedback controller with position, velocity and acceleration feedback is successfully implemented as the continuous controller. Switching algorithm of control parameter using learning vector quantization neural network (LVQNN) is newly proposed, which estimates the external loads of the pneumatic actuator. The effectiveness of the proposed control algorithms are demonstrated through experiments with various loads.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2484-2491
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• 2009

This paper proposes robust state feedback control of asynchronous sequential machines with model uncertainty. The considered asynchronous machine is deterministic, but its state transition function is partially known before executing a control process. The main objective is to derive the existence condition for a corrective controller for which the behavior of the closed-loop system can match a prescribed model in spite of uncertain transitions. The proposed control scheme also has learning ability. The controller perceives true state transitions as it undergoes corrective actions and reflects the learned knowledge in the next step. An adaptation is made such that the controller can have the minimum number of state transitions to realize a model matching procedure. To demonstrate control construction and execution, a VHDL and FPGA implementation of the proposed control scheme is presented.