• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.9
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• pp.1245-1247
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• 2014

This paper presents a robust position tracking controller for motor-driven flexible joint manipulators using only the motor angle measurement. The control problem is not easy because the link position is hard to estimate in the presence of parameter uncertainties. The proposed controller consists of a feedback linearization controller (FLC) and two proportional-integral observers (PIOs) that estimate both system states including the link position and an equivalent disturbance for compensating the parameter uncertainties. Comparative computer simulations are conducted to demonstrate the effectiveness of the proposed control algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2365-2370
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• 2008

In this paper, applications of wavelet neural network controller to position control of nonlinear system are considered. Wavelet neural network is used in the objectives which improve the efficiency of LQR controllers. It is possible to make unstable nonlinear systems stable by using LQR(Linear Quadratic Regulator) technique. And, in order to be adapted to disturbance effectively in this system it uses wavelet neural network controller. Applying this method to the position control of nonlinear system, its usefulness is verified from the results of experiment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.1095-1096
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• 2010

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.2078-2090
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• 2001

This paper deals with a precise position synchronous control by a cooperative control method of two axes rotating systems. First, the system's dynamics including motor drives described by a motor circuit equation and Newton's kinetic formulation about rotating system. Next, based on conventional PID(Proportional, Integral, Derivative) control law, current and speed controller are designed very simply to follow up reference speed correctly under some disturbances. Also, position synchronous controller designed to minimize position errors according to integration of speed errors between two motors. Then, the proposed control enables the distributed drives by a software control algorithm to behave in a way as if they are mechanically hard coupled in axes. Further, the stabilities and robustness or the proposed system are investigated. Finally, the proposed system presented here is shown to be more precise position synchronous motion than conventional systems through some simulations and experiments.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.9-15
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• 1999

A piezoelectric actuator is widely used in precision positioning applications due to its excellent positioning resolution. However, the piezoelectric actuator lacks in repeatability because of its inherently high hysteresis characteristic between voltage and displacement. In this paper, a controller is proposed to compensate the hysteresis nonlinearity. The controller is composed of a PID and a neural network part in parallel manner. The output of the PID controller is used to teach the neural network controller by the unsupervised learning method. In addition, the PID controller stabilizes the piezoelectric actuator in the beginning of the learning process, when the neural network controller is not learned. However, after the learning process the piezoelectric actuator is mainly controlled by the neural netwok controller. In this paper, the excellent tracking performance of the proposed controller was verified by experiments and was compared with the classical PID controller.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.756-762
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• 2006

Shape Memory Alloy(SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications such as aeronautics, surgical tools, robotics and so on. Although the conventional PID controller can be used with slow response systems, there has been limited success in precise motion control of SMA actuators, since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis and changes in the surrounding environment of the systems. This paper presents a new development of a SMA position control system by using a self-tuning fuzzy PID controller. This control algorithm is used by tuning the parameters of the PID controller thereby integrating fuzzy inference and producing a fuzzy adaptive PID controller, which can then be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self-tuning fuzzy PID controllers are both included in this paper.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.281-284
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• 1996

Samsung Electronics has developed high performance velocity and position controllers for induction motors, and succeeded in mass production for the first time in Korea. Dynamic performance and final control accuracy of the controller are equivalent to those of AC servo motor controller. At present, we adopted the controller as spindle motor drive for Samsung CNC systems, and expect its wide use in industry as general purpose velocity and position controller for induction motor.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.1
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• pp.79-86
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• 2012

This paper proposes PID and RIC (Robust Internal-loop Compensator) based motion controller using dual learning algorithm for position control of linear synchronous motor respectively. Its gains are auto-tuned by using two learning algorithms, reinforcement learning and neural network. The feedback controller gains are tuned by reinforcement learning, and then the feedforward controller gains are tuned by neural network. Experiments prove the validity of dual learning algorithm. The RIC controller has better performance than does the PID-feedforward controller in reducing tracking error and disturbance rejection. Neural network shows its ability to decrease tracking error and to reject disturbance in the stop range of the target position and home.

• Transactions of The Korea Fluid Power Systems Society
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• v.7 no.4
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• pp.23-31
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• 2010

As the analysis and design technologies for electro-magnetic actuation has advanced over the years, proportional solenoid valve is gaining acceptance in wide range of industrial and commercial applications because of its superior characteristics over the conventional AOV or MOV, such as improved performance, reduced maintenance costs. This research deals with the position controller design of two-stage flow control solenoid valve. Investigation of steady-state characteristics and dynamic model identification for pilot disc is performed. Least square method to minimize the error magnitude of frequency response between the closed-loop and target system is applied to the design of PI-controller gains. From the experiments of step and frequency response, it is concluded that the controller meets the performance specification of target system, which verifies the usefulness of controller design method for proportional solenoid valve.

• Journal of the Korean Institute of Navigation
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• v.23 no.3
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• pp.35-44
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• 1999

This research aims to seek active control of ball-beam position stability by resorting to neural networks whose layers are given bias weights. The controller consists of an LQR (linear quadratic regulator) controller and a neural networks controller in parallel. The latter is used to improve the responses of the established LQR control system, especially when controlling the system with nonlinear factors or modelling errors. For the learning of this control system, the feedback-error learning algorithm is utilized here. While the neural networks controller learns repetitive trajectories on line, feedback errors are back-propagated through neural networks. Convergence is made when the neural networks controller reversely learns and controls the plant. The goals of teaming are to expand the working range of the adaptive control system and to bridge errors owing to nonlinearity by adjusting parameters against the external disturbances and change of the nonlinear plant. The motion equation of the ball-beam system is derived from Newton's law. As the system is strongly nonlinear, lots of researchers have depended on classical systems to control it. Its applications of position control are seen in planes, ships, automobiles and so on. However, the research based on artificial control is quite recent. The current paper compares and analyzes simulation results by way of the LQR controller and the neural network controller in order to prove the efficiency of the neural networks control algorithm against any nonlinear system.