• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.960-967
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• 2008

This paper shows that design of a robustly stable repetitive control system is equivalent to that of a feedback control system for an uncertain linear time-invariant system satisfying the well-known robust performance condition. Once a feedback controller is designed to satisfy the robust performance condition, the feedback controller and the repetitive controller using the performance weighting function robustly stabilizes the repetitive control system. It is also shown that we can obtain a steady-state tracking error described in a simple form without time-delay element if the robust stability condition is satisfied for the repetitive control system. Moreover, using this result, a sufficient condition is provided, which ensures that the least upper bound of the steady-state tracking error generated by the repetitive control system is less than or equal to the least upper bound of the steady-state tracking error only by the feedback system.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.263-272
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• 2000

In this paper we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastcic approximation method it is regarded as a stochastic recursive filter algorithm. In addition we provide a filtering and control condition for a stochastic nonlinear system called the perfect filtering condition in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable and the proposed neural controller is more efficient than the conventional LQG controller and the canonical LQ-Neural controller.

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.112-117
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• 2013

The $\overline{x}$ control chart for the process mean and either the R or s control chart for the process dispersion have been used together to monitor the manufacturing processes. However, it has been pointed out that this procedure is flawed by a fault that makes it difficult to capture the behavior of process condition visually by considering the relationship between the shift in the process mean and the change in the process dispersion because the respective characteristics are monitored by an individual control chart in parallel. Then, the ($\overline{x}$, s) control chart has been proposed to enable the process managers to monitor the changes in the process mean, process dispersion, or both. On the one hand, identifying which process parameters are responsible for out-of-control condition of process is one of the important issues in the process management. It is especially important in the ($\overline{x}$, s) control chart where some parameters are monitored at a single plane. The previous literature has proposed the multiple decision method based on the statistical hypothesis tests to identify the parameters responsible for out-of-control condition. In this paper, we propose how to identify parameters responsible for out-of-control condition using the information criterion. Then, the effectiveness of proposed method is shown through some numerical experiments.

• Physical Therapy Korea
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• v.19 no.3
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• pp.40-50
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• 2012

The purpose of this study was to investigate the effect of visual feedback on the postural control of stroke patients, by systematically varying conditions of visual feedback [eye-open condition (EO) vs. eye-closed condition (EC)], and base-support (both-side support, affected-side support, and unaffected-side support). In this study, we allocated 41 stroke patients with no damage in the cerebellum and visual cortex who can walk at least 10 meters independently, and 35 normal adults who have no experience of stroke to the control group. Both groups were asked to perform a "sit-to-stand" task three to five times, and their postural control ability was measured and compared in terms of asymmetric dependence (AD) instead of the traditional symmetric index (SI) in the literature. The results showed that although both subject groups maintained better postural control in the EO condition than in the EC condition, the patient group appeared to be more stable in EC than in EO when they were required to perform the task of the support condition given on the affected side. These results implied that visual feedback can impair stroke patients' postural control when it is combined with a specific support condition.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.608-615
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• 1998

In this paper a second-order iterative learning control algorithm with feedback is proposed for the trajectory-tracking control of nonlinear dynamic systems with unidentified parameters. In contrast to other known methods, the proposed teaming control scheme utilize more than one past error history contained in the trajectories generated at prior iterations, and a feedback term is added in the learning control scheme for the enhancement of convergence speed and robustness to disturbances or system parameter variations. The convergence proof of the proposed algorithm is given in detail, and the sufficient condition for the convergence of the algorithm is provided. We also discuss the convergence performance of the algorithm when the initial condition at the beginning of each iteration differs from the previous value of the initial condition. The effectiveness of the proposed algorithm is shown by computer simulation result. It is shown that, by adding a feedback term in teaming control algorithm, convergence speed, robustness to disturbances and robustness to unmatched initial conditions can be improved.

• 전기의세계
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• v.19 no.2
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• pp.21-23
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• 1970

Necessary conditions of optimal distributed parameter control systems, Hamiltons coanonical equations, welerstress condition, transversality condition and boundary condition are obtained, when the control function is constrained and the performance index takes on the general form. Also it is concluded that the lumped parameter system is the special case of the distributed parameter system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.346-352
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• 1998

In this paper, we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastic approximation method, it is regarded as a stochastic recursive filter algorithm. In addition, we provide a filtering and control condition for a stochastic nonlinear system, called perfect filtering condition, in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence, the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable. and the proposed neural controller is more efficient than the conventional LQG controller and the canoni al LQ-Neural controller.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.779-782
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• 1996

Repetitive control is a proposed control strategy in view of the internal model principle and achieves a high accuracy asymptotic tracking property by implementing a model that generates the periodic signals of period into the closed-loop system. Since the repetitive control system contains a periodic signal generator with positive feedback loop, which reduces the stability margin, in the overall closed-loop system, the stability of the closed-loop system should be considered as an important problem. In case that a real system has plant uncertainties which are not represented through modeling, the robust stability problem of the repetitive control system has not been considered sufficiently. In this paper, we propose the robust stability condition for the system with modeling uncertainty. The proposed robust stability condition will be obtained using the robust performance condition in the H$_{\infty}$ control. Moreover, by use of the proposed robust stability condition, we propose a procedure that designs a repetitive controller and a feedback controller simultaneously which can stabilize the overall closed-loop system robustly and which can also do the closedloop system without repetitive controller..

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.553-556
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• 2005

A novel sufficient condition of discretized equivalent control based sliding mode controller (SMC) for a second-order system with external disturbance to be globally uniformly ultimately bounded (GUUB) is proposed. The proposed stability condition guarantees that the system state is always GUUB in the presence of disturbance. The ultimate bounds of the system state variables are also derived.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.1-6
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• 2017

Process capability is well known in quality control literatures. Process capability refers to the uniformity of the process. Obviously, the variability in the process is a measure of the uniformity of output. It is customary to take the 6-sigma spread in the distribution of the product quality characteristic as a measure of process capability. However there is no reference of process capability when maximum material condition is applied to datum and position tolerance in GD&T (Geometric Dimensioning and Tolerancing). If there is no material condition in datum and position tolerance, process capability can be calculated as usual. If there is a material condition in a feature control frame, bonus tolerance is permissible. Bonus tolerance is an additional tolerance for a geometric control. Whenever a geometric tolerance is applied to a feature of size, and it contains an maximum material condition (or least material condition) modifier in the tolerance portion of the feature control frame, a bonus tolerance is permissible. When the maximum material condition modifier is used in the tolerance portion of the feature control frame, it means that the stated tolerance applies when the feature of size is at its maximum material condition. When actual mating size of the feature of size departs from maximum material condition (towards least material condition), an increase in the stated tolerance-equal to the amount of the departure-is permitted. This increase, or extra tolerance, is called the bonus tolerance. Another type of bonus tolerance is datum shift. Datum shift is similar to bonus tolerance. Like bonus tolerance, datum shift is an additional tolerance that is available under certain conditions. Therefore we try to propose how to calculate process capability index of position tolerance when maximum material condition is applied to datum and position tolerance.