• Journal of Magnetics
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• v.19 no.1
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• pp.28-31
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• 2014

The aim of the present study was to examine whether mirror therapy, in conjunction with repetitive transcranial magnetic stimulation (rTMS), can improve the upper extremity function of stroke patient. This study was conducted with 35 subjects, who were diagnosed as a hemiparesis by stroke. The Mirror plus rTMS group was of 12 members who undertook mirror therapy in conjunction with rTMS, the Mirror group was of 11 members who undertook mirror therapy, and the control group was of 12 members who undertook sham therapy. A motor cortex excitability was performed by motor evoked potential, and upper limb function was evaluated by Fugl-Meyer Assessment, and Box and Block Test. Significant difference was shown after the experiment, in comparison of the groups in terms of latency, and as the result of post hoc test, significant difference was shown between the Mirror plus rTMS group and control group, and between the Mirror group and control group, respectively. Significant difference was shown after the experiment in comparison of the groups in amplitude, and as the result of post hoc test, significant difference was shown between the Mirror plus rTMS group and Mirror group, and between the Mirror plus rTMS group and control group. Significant difference was shown after the experiment, in comparison of the groups in FMA and BBT, and as the result of post hoc test, significant difference was shown between the Mirror plus rTMS group and Mirror group, and between the Mirror group and control group. The study showed that mirror therapy in conjunction with rTMS is more effective to improve upper extremity function, than mirror therapy and sham therapy.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.299-302
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• 1996

A control algorithm is proposed for nonlinear multi-input multi-output(MIMO) batch processes by combining quadratic iterative learning control(Q-ILC) with model predictive control(MPC). Both controls are designed based on output feedback and Kalman filter is incorporated for state estimation. Novelty of the proposed algorithm lies in the facts that, unlike feedback-only control, unknown sustained disturbances which are repeated over batches can be completely rejected and asymptotically perfect tracking is possible for zero random disturbance case even with uncertain process model.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.2
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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.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.96-100
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• 2003

In this paper, a DLC method is proposed for linear feedback systems to improve the tracking performance when the task of the 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 considered in this paper have some relations called proportionality and it is shown by mathematical analysis that DLC can be utilized to generate 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.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.234-239
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• 1993

Most of the control problem is for the redundant manipulators use the pseudo-inverse control, thit is, the redundancy is resolved by the pseudo-inverse of the Jacobian matrix and then the controller is designed based on this resolution. However, this pseudo-inverse control has some problems when the redundant robot repeats the cyclic tasks. This is because the pseudo-inverse resolution is a local solution that generates the different configurations of the robot arm for the same hand position. Therefore it is necessary to find the global solution that maintains the optimal configuration of the robot for the repetitive tasks. In this paper, we want to propose a redundancy resolution method by the optimal theory that uses the calculus of variation. The problem formulations are : first to convert the optimal resolution problem to an optimal control problem and then to resolve the redundancy using the necessary conditions of optimal control.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.4
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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.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.194-203
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• 2008

Iterative learning control (ILC) is a simple and effective method for the control of systems that perform the same task repetitively. ILC algorithm uses the repetitiveness of the task to track the desired trajectory. In this paper, we propose a PID (proportional plus integral and derivative) type ILC update law for control discrete-time single input single-output (SISO) linear time-invariant (LTI) systems, performing repetitive tasks. In this approach, the input of controlled system in current cycle is modified by applying the PID strategy on the error achieved between the system output and the desired trajectory in a last previous iteration. The convergence of the presented scheme is analyzed and its convergence condition is obtained in terms of the PID coefficients. An optimal design method is proposed to determine the PID coefficients. It is also shown that under some given conditions, this optimal iterative learning controller can guarantee the monotonic convergence. An illustrative example is given to demonstrate the effectiveness of the proposed technique.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.2
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• pp.83-88
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• 2006

This paper proposes a method that minimizes the consumed energy by searching the optimal locations of the mass centers of the biped robot's links using Genetic Algorithm. This paper presents a learning controller for repetitive gait control of the biped robot. The learning control scheme consists of a feedforward learning nile and linear feedback control input for stabilization of learning system. The feasibility of learning control to the biped robotic motion is shown via computer simulation and experimental results with 24 DOF biped walking robot.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.540-543
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• 1997

In this paper, a precise trajectory tracking method for mobile robot using a vision system is presented. In solving the problem of precise trajectory tracking, a hierarchical control structure is used which is composed of the path planer, vision system, and dynamic controller. When designing the dynamic controller, non-ideal conditions such as parameter variation, frictional force, and external disturbance are considered. The proposed controller can learn bounded control input for repetitive or periodic dynamics compensation which provides robust and adaptive learning capability. Moreover, the usage of vision system makes mobile robot compensate the cumulative location error which exists when relative sensor like encoder is used to locate the position of mobile robot. The effectiveness of the proposed control scheme is shown through computer simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1039-1042
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• 1995

In real time application such as motion control, it is hard to find the application of non-causal filtering due to its need for future position data, even though it shows wide usage in off-line digital signal processing. Recently, some of motion control areas such as learning and repetitive control use non-causal filtering technique in their application. these kinds of zero-lag non-causal filter application are very usful not only to reduce the machine vibration, but also to increase control accuracy with comparatively less work. In this paper, genuine method to implement zero-lag non-causal filter in a CNC is introduced. Also the variation of this implementation for the learning operation is suggested to give the NC better control performance for a specific job. By adopting the new NC architecture call Soft-NC, all these implementions are made possible here, and especially large memory requirement which hinders their usage for many years is no longer barrier in their real world application.