• Proceedings of the KIPE Conference
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• 2004.07b
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• pp.738-741
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• 2004

The overall performance of AC servo system is greatly affected by the uncertainties of unpredictable mechanical parameter variations and external load disturbances. Therefore, to compensate this problem, it is necessary to know different parameters and load disturbances subjected to position/speed control. This paper proposes an online identification method of mechanical parameters/load disturbances for AC servo system using Support Vector Regression (SVR). The proposed methodology advocates analytic parameter regression directly from the training data, rather than adaptive controller and observer approaches commonly used in motion control applications. The experimental results demonstrate that the proposed SVR algorithm is appropriate for control of unknown servo systems even with large measurement noise.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.2
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• pp.31-37
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• 1982

A practical and robust control scheme is suggested for MIMO disciete time processes with real simple poles. This type of control scheme, having the advantages of both the adaptiveness and optimality, maybe successfully applicable to structured dynamic controllers for plants whose paiameters are slowly timevaiying. The identiflcation of the process paiameters is undertaken in ARMA form and the optimization of the feedback gain matrix is performed in the state space representation with respect to a standard quadratic criterion.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.293-295
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• 1995

In this article a fuzzy controller and neural network adaptive observer is proposed and applied to the case of induction motor control. The proposed observer which comprises neural network flux observer and neural network torque observer is trained to learn the flux dynamics and torque dynamics and subjected to further on-line training by means of a backpropagation algorithm. Therefore it has been shown that the robust control of induction motor neglects the rotor time constant variations.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.39-50
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• 2017

Moving object tracking techniques can be considered as one of the most essential technique in the video understanding of which the importance is much more emphasized recently. However, irregularity of light condition in the video, variations in shape and size of object, camera motion, and occlusion make it difficult to tracking moving object in the video. Swarm based methods are developed to improve the performance of Kalman filter and particle filter which are known as the most representative conventional methods, but these methods also need to consider dynamic property of moving object. This paper proposes adaptive parameter control method which can dynamically change weight value among parameters in particle swarm optimization. The proposed method classifies each particle to 3 groups, and assigns different weight values to improve object tracking performance. Experimental results show that our scheme shows considerable improvement of performance in tracking objects which have nonlinear movements such as occlusion or unexpected movement.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• Earthquakes and Structures
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• v.11 no.6
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• pp.943-966
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• 2016

Recently, magnetorheological elastomer (MRE) material and its devices have been developed and attracted a good deal of attention for their potentials in vibration control. Among them, a highly adaptive base isolator based on MRE was designed, fabricated and tested for real-time adaptive control of base isolated structures against a suite of earthquakes. To perfectly take advantage of this new device, an accurate and robust model should be built to characterize its nonlinearity and hysteresis for its application in structural control. This paper first proposes a novel hysteresis model, in which a nonlinear hyperbolic sine function spring is used to portray the strain stiffening phenomenon and a Voigt component is incorporated in parallel to describe the solid-material behaviours. Then the fruit fly optimization algorithm (FFOA) is employed for model parameter identification using testing data of shear force, displacement and velocity obtained from different loading conditions. The relationships between model parameters and applied current are also explored to obtain a current-dependent generalized model for the control application. Based on the proposed model of MRE base isolator, a second-order sliding mode controller is designed and applied to the device to provide a real-time feedback control of smart structures. The performance of the proposed technique is evaluated in simulation through utilizing a three-storey benchmark building model under four benchmark earthquake excitations. The results verify the effectiveness of the proposed current-dependent model and corresponding controller for semi-active control of MRE base isolator incorporated smart structures.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.858-863
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• 1988

A micro-processor based control system for a brushless DC motor used in the motor-driven electromechanical total artificial heart was developed. Functionally, the control system is composed of two parts. The first part is the velocity and position controller to assure that the motor follows a predetermined optimal velocity profile with minimal energy consumption, and to guarantee the full stroke length. This part also utilize the passive adaptive control method to be robust against the load disturbance, system parameter variation, and uncertainty which is the environment of artificial heart system. The pump output control is the second part, and this part provides the required responses of the artificial heart to the time-varying physiologic demands. The basic requirements of these responses are preload sensitivity, afterload insensitivity, and the balanced ventricular outputs. The performance and reliability of this control system was evaluated through a series of mock circulation tests and animal implantation, and the results are very encouraging.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1978-1983
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• 2011

BLDC motor is used in a wide variety of industrial and servo applications. Its features and advantages mainly consist in high value of torque/inertia ratio, high efficiency with speed range and high dynamic performance. This paper deals with the speed control of a trapezoidal type brushless DC motor using Fuzzy PI controller. The conventional PI controller has been widely used in industrial applications. If we select a optimal PI control gain, the PI controller shows very good control performance. But it is very difficult to find the optimal PI control gain. Fuzzy control does not need any model of plant and is basically adaptive and gives robust performance for plant parameter variation. Therefore the combinations of conventional PI controller and fuzzy controller seem to be very effective. This paper deals with PI controller with 4-rule based fuzzy controller. The proposed fuzzy PI controller increases the control performance of the conventional PI controller. Simulation and experimental results show that fuzzy PI controller has a good robustness regarding the improper tuned PI controller.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.28-36
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• 1995

In this paper, a double loop control model using leaky delay LMS algorithm are proposed for active noise control. The proposed double loop control model estimates the loudspeaker characteristic and the error path transfer function with on-line using only gain and acoustic time delay to reduce computation burden. The control of error signal through double loop control scheme makes the more robust cntrol system. The input signal of filter to estimate acoustic time delay is used difference between input signal of input microphone and adaptive filter output. And also, in nonstationary environments, the leaky delay LMS algorithm is employed to counteract parameter drift of delay LMS algorithm. For practical noise signal, the proposed double loop control model reduces noise level about 12.9 dB.