• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.228-228
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• 2000

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require an accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller Parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is obtained through a series of experiments for the various types of input while applying disturbances to the cylinder. .and performance of this controller was compared with that of PID, PD controller. As a experimental result, it can be proven that the position tracking performance of the neuro-fuzzy is better than that of PID and PD controller.

• Journal of Power System Engineering
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• v.6 no.1
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• pp.96-101
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• 2002

In this paper, we address the problem of controlling an end-effector to track and grab a moving target using the visual servoing technique. A visual servo mechanism based on the image-based servoing principle, is proposed by using visual feedback to control an end-effector without calibrated robot and camera models. Firstly, we consider the control problem as a nonlinear least squares optimization and update the joint angles through the Taylor Series Expansion. And to track a moving target in real time, the Jacobian estimation scheme(Dynamic Broyden's Method) is used to estimate the combined robot and image Jacobian. Using this algorithm, we can drive the objective function value to a neighborhood of zero. To show the effectiveness of the proposed algorithm, simulation results for a six degree of freedom robot are presented.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.2
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• pp.117-123
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• 2013

Since the nonlinear factors such as friction in a mechanical servo system can't be easily measured nor estimated accurately. Therefore, it is difficult to compensate friction correctly. Friction makes a significant error in a 2-axis stabilized gimbal system and finally fails to reach the ultimate control performance goals. To solve these problems, lots of studies on the control methods applying observer have been performed. However, these methods can be used in specific conditions and are limited to apply them to the accurate 2-axis stabilized gimbal system in military sector. This paper deals with the PI-LEAD algorithm which is modified with a general and robust PID algorithm, proves the effect of the algorithm through modeling and simulation, and verifies the performance by applying the algorithm to the real 2-axis stabilized system. It is verified through the performance test that the PI-LEAD algorithm minimizes the error caused by friction and meets requirements of the accurate servo system.

• Journal of Drive and Control
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• v.13 no.3
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• pp.15-23
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• 2016

The direct-drive servo valve(DDV) is a kind of one-stage valve because the main spool valve is directly driven by the dc motor. Since the DDV structure is simple, it is less expensive, more reliable, and offers a reduced internal leakage and a reduced sensitivity to fluid contamination. The control system of the DDV is highly nonlinear due to a current limiter, a voltage limiter, and the flow-force effect on the spool motion. The shape of the step response of the DDV-control system varies considerably according to the magnitudes of the step input and the load pressure. The system-design requirements mean that the overshoots should be less than 20%, and the errors at 0.02s should be less than 2%, regardless of the reference-step input sizes of 1V and 5V and the load-pressure magnitudes of 0MPa and 20.7MPa. To satisfy the system-design requirements, the PID-controller parameters of $K_c$, $T_i$ and $T_d$, and the derivative-feedback gain of $K_{der}$ are designed using the root locus and manual tuning.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.75.5-75
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• 2001

In this paper, we study design of a ILQ(Inverse Linear Quadratic optimal control) looper control system for hot strip mills. The looper which is placed between stands plays an important role in controlling strip width by regulating strip tension variation generated from the velocity difference of main work rolls. A Looper servo controller is designed by ILQ control theory which is an inverse problem of LQ(Linear Quadratic optimal control) control. The mathematical model for looper system is obtained by Taylor´s linearization of nonlinear differential equations. Then we designed linear controller for linearization model by using the ILQ control algorithm. Thereafter this controller is applied to the nonlinear model for model identification. As a result, we show the controller´s robustness for the model error, external disturbance and sensor noise.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.1
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• pp.52-59
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• 1998

This paper represents identification and control designs using neural networks for a class of nonlinear dynamic systems. A proposed neuro-controller is a combination of a linear controller and a neural network, and is trained by indirect neuro-control scheme. The proposed neuro-controller is implemented and tested on an IBM PC-based bar system, and is applicable to many dc-motor-driven precisiion servo mechanisms. The ideas, algorithm, and experimental results are described. Experimental resutls are shown to be superior to those of conventional control.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.344-346
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• 1995

In this paper a drive strategy of AC Servo Motor using Fuzzy method was proposed. Since the transfer function of the plant is nonlinear and very complicated, there are difficultly in driving the system with real time. The performance of out method is confirmed by computer simulation and experimental results. The high performance and high accuracy of the driving system. Fuzzy is designed and proposed.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.2
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• pp.114-123
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• 2005

A nonlinear speed control of a PMSM using a sequential parameter auto-tuning algorithm for servo equipments is presented. The nonlinear control scheme gives an undesirable output performance under the mismatch of the system parameters and load conditions. Recently, to improve the performance, an adaptive linearization scheme, a sliding mode control and an observer-based technique have been reported. Although a good performance can be obtained, the performance is not satisfactory any more under specific conditions such as a large inertia variation, a fast speed transient or an increased sampling time. The simultaneous estimation of principal parameters giving a direct influence on speed dynamics is generally not simple. To overcome this problem, a a sequential parameter auto-tuning algorithm at start-up is proposed, where dominant parameters are estimated in a prescribed regular sequence based on the method that one parameter is estimated during each interval. The proposed scheme is implemented on a PMSM using DSP TMS320C31 and the effectiveness is verified through simulations and experiments.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1409-1423
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• 2018

Compared with classical linear controllers, a nonlinear controller can result in better control performance for the nonlinear uncertainties of continuously variable transmission (CVT) systems that are driven by a synchronous reluctance motor (SynRM). Improved control performance can be seen in the nonlinear uncertainties behavior of CVT systems by using the proposed mingled revised recurrent Hermite polynomial neural network (MRRHPNN) control with mend ant colony optimization (ACO). The MRRHPNN control with mend ACO can carry out the overlooker control system, reformed recurrent Hermite polynomial neural network (RRHPNN) control with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, to help improve convergence and to obtain better learning performance, the mend ACO is utilized for adjusting the two varied learning rates of the two parameters in the RRHPNN. Finally, comparative examples are illustrated by experimental results to confirm that the proposed control system can achieve better control performance.

• Journal of the Korean Society for Precision Engineering
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• v.6 no.2
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• pp.65-76
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• 1989

The objective of this paper is to design the variable structure system(VSS) controller for the tracking control of excavator which is driven by electro-hydraulic servomechansim. It is generally agreed that the dynamic characteristics of the robot arm such as excavator are coupled, time varying, and highly nonlinear, and also hydraulic system contains nonlinear characteristics in itself, so performing exact position control and trajectory tracking control need remarkable consideration. To solve this porblem, this system was designed as a variable structure system. The salient feature of VSS is that the sliding mode occur on a switching surface. While in sliding mode, the system remains insensitive to parameter variations and disturbances. This control algorithm was applied to a hydraulic excavator by simulaltion and to a simulator by experiment. And its effectiveness was verified. And the results of VSS for the electro-hydraulic excavator was compared with that of the PID when load disturbances and system parameter variations exist.