• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.521-526
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• 2009

In this paper we are interested in the control of Doubly Fed Induction Machine (DFIM) using the Passivity Based Control (PBC). This work presents a solution to the problem of DFIM that requires a state observer. The proposed method shows very important advantages for nonlinear systems, especially in the trajectory tracking to achieve the needed DFIM performance. In the obtained results, the passivity provides high efficiency in DFIM based system, namely in its stability and robustness. An improvement behavior has been observed in comparison to the results given by the RST controller.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.1
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• pp.7-12
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• 2012

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2827-2829
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• 2005

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• Proceedings of the KIEE Conference
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• 1999.07f
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• pp.2467-2469
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• 1999

In this paper. the stability control system of constant output DC-DC converter is composed of considering a ripple source input and a transient variable sinusoidal power. Also, we design I-PD controller and add phase shift controller. Therefore, it is proposed controller that is stable about the input voltage and load alteration and tracking desired value.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2498-2500
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• 2004

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.347-350
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• 1992

In this note, we propose a method for designing a robot controller which can suppress the effects of both the model uncertainty and noisy velocity measurements. The controller is an output feedback compensator of which the constant gains are given in terms of a Riccati equation and a Lyapunov equation. The controller guarantees not only uniform boundedness but uniform ultimate boundedness. The stability result is local but the region can be arbitrarily enlarged at the expense of large control gain. The control law needs neither the exact knowledge of the physical robot parameters nor clean velocity measurements.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.7 s.172
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• pp.94-101
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• 2005

This work proposes a new method for describing the hysteresis non-linearity of a piezoelectric actuator. The hysteresis behaviour of piezoelectric actuators, including the minor loop trajectory, are modeled by geometrical relationship between a reference major loop and its minor loops. This hysteresis model is transformed into inverse hysteresis model in order to output compensated voltage with regard to the given input displacement. A feedforward neural network, which is trained by a feedback PID control module, is incorporated to the inverse hysteresis model to compensate unknown dynamics of the piezoelectric system. To show the feasibility of the proposed feedforward-feedback controller, some experiments have been carried out and the tracking performance was compared to that of simple PTD controller.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.580-585
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• 2010

In this paper, The object Tracking System is designed by SOPC based Nios II embedded processor and C2H compiler. And this system using single PTZ camera can effectively control IPs in the platform of SOPC based Nios II Embedded Processor and creating IP by C2H(C-To-Hardware) compiler for image-in/output, image-processing and devices of communication that can supply various monitoring information to network or serial. Accordingly, Special quality and processing speed of object tracking using high-quality algorism in the system is improved by hardware/software programming methods.

• Journal of Power Electronics
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• v.16 no.1
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• pp.297-309
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• 2016

Grid-connected inverters (GCIs) with an LCL output filter have the ability of attenuating high-frequency (HF) switching ripples. However, by using only grid-current control, the system is prone to resonances if it is not properly damped, and the current distortion is amplified significantly under highly distorted grid conditions. This paper proposes a synchronous reference frame equivalent proportional-integral (SRF-EPI) controller in the αβ stationary frame using the parallel virtual resistance-based active damping (PVR-AD) strategy for grid-interfaced distributed generation (DG) systems to suppress LCL resonance. Although both a proportional-resonant (PR) controller in the αβ stationary frame and a PI controller in the dq synchronous frame achieve zero steady-state error, the amplitude- and phase-frequency characteristics differ greatly from each other except for the reference tracking at the fundamental frequency. Therefore, an accurate SRF-EPI controller in the αβ stationary frame is established to achieve precise tracking accuracy. Moreover, the robustness, the harmonic rejection capability, and the influence of the control delay are investigated by the Nyquist stability criterion when the PVR-based AD method is adopted. Furthermore, grid voltage feed-forward and multiple PR controllers are integrated into the current loop to mitigate the current distortion introduced by the grid background distortion. In addition, the parameters design guidelines are presented to show the effectiveness of the proposed strategy. Finally, simulation and experimental results are provided to validate the feasibility of the proposed control approach.