• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.4
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• pp.22-31
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• 2005

The purpose of this study is to design a flexural structure that has a function of pan and tilt for an artificial eyeball. The artificial eyeball system has a function of image stabilization, which compensate panning and tilting vibration of the body on which the artificial eyeball is attached. The target closed loop control bandwidth is 50Hz, so the mechanical resonance frequency is required to be more than the control bandwidth, which is a tough design problem because of a big mass of camera and actuator. In this study, the design process including the selection of the principal parameters by numerical analysis with ANSYS will be described, as well as the design results and frequency response.

• Journal of Advanced Navigation Technology
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• v.18 no.2
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• pp.107-113
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• 2014

In this paper, controller Propose to prevent compressor surge and improve the transient response of the fuel flow control system of turbojet engine. Turbojet engine controller is designed by applying Artificial Neural Network PID control algorithm and make an inference by applying Artificial Neural Network Error Back Propagation Algorithm. To prevent any surge or a flame out event during the engine acceleration or deceleration, the ANN PID controller effectively controls the fuel flow input of the control system. ANN PID results are used as the fuel flow control inputs to prevent compressor surge and flame-out for turbo-jet engine and the controller is designed to converge to the desired speed quickly and safely. Using MATLAB to perform computer simulations verified the performance of the proposed controller. Response characteristics pursuant to the gain were analyzed by simulation.

• Journal of Chest Surgery
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• v.28 no.6
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• pp.542-548
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• 1995

The goal of this study is to develop an effective control system for cardiac output regulation based upon the preload and afterload conditions without any transducers and compliance chambers in the moving actuator type total artificial heart. Motor current waveforms during the actuator movement are used as an input to the automatic control algorithm. While the current waveform analysis is performed, the stroke length and velocity of the actuator are gradually increased up to the maximum pump output level. If the diastolic filling rate of either right or left pump begins to exceed the venous return, atrial collapse will occur. Since the diastolic suction acts as a load to the motor, this critical condition can be detected by analyzing the motor current waveforms. Every time this detection criterion is met, the control algorithm decreases the stroke velocity and length of the actuator step by step just below the critical detection level. Then, they start to increase. In this way the maximum pump output under given venous return can be achieved. Additionally the control algorithm provides some degree of afterload sensitivity. If the aortic pressure is detected to exceed 120 mmHg, the stroke length and velocity decrease in the same way as the response to the preload. Left-right pump output balance is maintained by proper adjustment of the asymmetry of the stroke angle. In the mock circulatory test, this control system worked well and there was a considerable range of stroke volume difference with adjustment of the asymmetry value. Two ovine experiments were performed. It was confirmed that the required cardiac output regulation according to the venous return could be achieved with adequate detection of diastolic function, at least in the in vivo short-term survival cases[2-3 days . We conclude that this control algorithm is a promising method to regulate cardiac output in the moving actuator type total artificial heart.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1042-1047
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• 2004

Voltage control is an essential part of the electric energy transmission and distribution system to maintain proper voltage limit at the consumer's terminal. Besides the generating units that provide the basic voltage control, there are many additional voltage-controlling agents e.g., shunt capacitors, shunt reactors, static VAr compensators, regulating transformers mentioned in [1], [2]. The most popular one, among all those agents for controlling voltage levels at the distribution and transmission system, is the on-load tap changer transformer. It serves two functions-energy transformation in different voltage levels and the voltage control. Artificial Neural Network (ANN) has been realized as a convenient tool that can be used in controlling the on load tap changer in the distribution transformers. Usage of the ANN in this area needs suitable training and testing data for performance analysis before the practical application. This paper briefly describes a procedure of processing the data to train an Artificial Neural Network (ANN) to control the tap changer operating decision of parallel transformers for a closed primary bus. The data set are used to train a two layer ANN using three different neural net learning algorithms, namely, Standard Backpropagation [3], Bayesian Regularization [4] and Scaled Conjugate Gradient [5]. The experimental results are presented including performance analysis.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.6
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• pp.1882-1892
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• 1996

This paper is concerned with the position control of the ond degree-of freedom manipulator using pneumatic artificial muscle actuator which is built to have a proper compliance. For t his pneumatic artificial muscle actuator though, it is difficult to make an effective control scheme due to the nonlinearity and uncertainties on the dynamics of the actuator. In this paper, a third-order equation of motion is derived for the actuator including the dynamics of the pneumatic servovalve. Later, various modeling uncertainties due to the nonlinearity and unmodeled dynamics of the servo vlave and the actuator are taken care of, as a trade-off between the closed-loop performance of the controlled system and its robustness to uncertainties. A controller using .mu. synthesis thchnique is designed, and robust performance against measurement noise, various modeling uncertainties due to the dynamics of the servo valve and actuator is achieved. The effectiveness of the proposed control methods is illustrated through simulations and experiments.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.572-575
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• 2005

A novel pneumatic artificial muscle actuator (PAM actuator), which has achieved increased popularity to provide the advantages such as high strength and high power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available and cheap power source, inherent safety and mobility assistance to humans performing tasks, has been regarded during the recent decades as an interesting alternative to hydraulic and electric actuators. In order to realize satisfactory control performance, a variable damper Magneto Rheological Brake (MRB), Is equipped to the Joint of the manipulator. Superb mixture of conventional PID controller and a phase plane switching control method brings us a novel controller. This proposed controller is appropriate for a kind of plants with nonlinearity, uncertainties and disturbances. The experiments were carried out in practical PAM manipulator and the effectiveness of the proposed control algorithm was demonstrated through experiments, which had proved that the stability of the manipulator can be improved greatly in a high gain control by using MRB with phase plane switching control method and without regard for the changes of external inertia loads.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.8
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• pp.627-635
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• 2012

This study aims at developing an artificial neural network(ANN)-based predictive and adaptive temperature control method to control the openings at internal and external skins, and heating systems used in a building with double skin envelope. Based on the predicted indoor temperature, the control logic determined opening conditions of air inlets and outlets, and the operation of the heating systems. The optimization process of the initial ANN model was conducted to determine the optimal structure and learning methods followed by the performance tests by the comparison with the actual data measured from the existing double skin envelope. The analysis proved the prediction accuracy and the adaptability of the ANN model in terms of Root Mean Square and Mean Square Errors. The analysis results implied that the proposed ANN-based temperature control logic had potentials to be applied for the temperature control in the double skin envelope buildings.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.5
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• pp.533-541
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• 2011

This paper presents a frequency-response test performed on an antagonistic actuation system consisting of two Mckibben pneumatic artificial muscles and a pneumatic circuit with pressure valves. Varying switching frequency to pressure valves from 0.1 Hz to 5 Hz, parameters of a linear model were estimated optimally to predict dynamic characteristics of the antagonistic actuation. A model-base control scheme with estimated parameters was built for the precise trajectory tracking of the antagonistic structure and realized on a reconfigurable embedded control system, CompactRIO. Experimental results showed that the proposed model-based control scheme gave good performance in trajectory tracking comparing with a PD control scheme when square wave and sinusoidal wave were given as references to follow.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.539-550
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• 2021

Purpose: The purpose of this study is to propose a multivariate CUSUM control chart that can detect the out-of-control state fast while monitoring the cross- and auto- correlated multivariate time series data. Methods: We first build models to estimate the observation data and calculate the corresponding residuals. After then, a multivariate CUSUM chart is applied to monitor the residuals instead of the original raw observation data. Vector Autoregression and Artificial Neural Net are selected for the modelling, and Separated-MCUSUM chart is selected for the monitoring. The suggested methods are tested under a number of experimental settings and the performances are compared with those of other existing methods. Results: We find that Artificial Neural Net is more appropriate than Vector Autoregression for the modelling and show the combination of Separated-MCUSUM with Artificial Neural Net outperforms the other alternatives considered in this paper. Conclusion: The suggested chart has many advantages. It can monitor the complicated multivariate data with cross- and auto- correlation, and detects the out-of-control state fast. Unlike other CUSUM charts finding their control limits by trial and error simulation, the suggested chart saves lots of time and effort by approximating its control limit mathematically. We expect that the suggested chart performs not only effectively but also efficiently for monitoring the process with complicated correlations and frequently-changed parameters.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.509-513
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• 1995

A microcontroller-based DC motor control system for a total artificial heart(TAH) was developed. Using a one-chip microcontroller, 87Cl96KB, the design of digital motor speed control system and servo control system is demonstrated. Functionally, the control system consists of a position control unit, a speed control unit, and a communication unit. The performance and the reliability of the developed control system were assessed through a series of mock circulation system experiments.