• Proceedings of the KIPE Conference
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• 2003.07a
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• pp.337-340
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• 2003

This paper presented practical details about control-loop design and dynamic analysis for a peak current-mode controlled asymmetrical half-bridge(ASHB) do-to-dc converter, Graphical loop gain method is used to design the feedback compensation and analyze the closed-loop performance of ASHB converter. The results of the control design and closed-loop analysis are validated by experiments on a prototype converter.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1078-1081
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• 1993

The Objective of this paper is to provide fuzzy control designers with a design tool for stable fuzzy logic controllers. Given multiple sets of data disturbed by vagueness uncertainty, we generate the implicative rules that guarantee stability and robustness of closed-loop fuzzy dynamic systems. We propose the cell-state transition method which utilizes Hsu's cell-to-cell mapping concept [1]. As a result, a generic and implementable design methodology for obtaining a fuzzy feedback gain K, a fuzzy hypercube [2], is provided and illustrated with simple examples.

• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.229-230
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• 2015

This paper presents practical details about control-loop design and dynamic analysis for a voltage-mode controlled isolated double step-down DC-DC converter. Graphical loop gain method is used to design the feedback compensation and analyze the closed-loop performance of isolated double step-down DC-DC converter. The results of the control design and closed-loop analysis are validated by experiments on a prototype converter.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.397-400
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• 1992

Many dynamic control have been proposed; however, most of them are limited within stage of simulation study. The main reason is that the computations required for inverse dynamics are far beyond the ability of the present commercially available microprocessors. In this paper, In order to achieve real-time processing in robot dynamic control, a parallel processing computer for robot dynamic control is implemented using two transputer. Two transputer compute two degree of freedom robot. The transputer is a special purpose MPU for parallel processing. Transputers are used in networks to build a high performance concurrent system. A network of transputers and peripheral controllers is constructed using point-to-point communication. To gain most benifit from the transputer architecture, the whole system is programmed in OCCAM which is a high level language for concurrent applications. This control algorithm is applied to the RHINO SCARA type manipulator. We could taked about 438.6 microseconds to compute robot dynamic with two-processors.

• Journal of Power Electronics
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• v.11 no.3
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• pp.369-374
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• 2011

Usually, a LPF (low pass filter) is used in the feedback loop of a SRF (synchronous reference frame) - PLL (phase locked loop) system because the measured grid voltage contains harmonic distortions and sensor noises. In this paper, it is shown that the cut-off frequency of the LPF should be designed to suppress the harmonic ripples contained in the measured voltage. Also, a new design method for the loop gain of the PI-type controller in the SRF-PLL is proposed with consideration of the dynamics of the LPF. As a result, a better transient response can be obtained with the proposed design method. The LPF frequency and the PI controller gain are designed in coordination according to the steady state and dynamic performance requirements. Furthermore, in the proposed method, the controller gain and the LPF cut-off frequency are changed from their normal value to a transient value when a voltage disturbance is detected. This paper shows the feasibility and usefulness of the proposed methods through the computer simulations and experimental results.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.195-203
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• 2016

In order to overcome the influence of system stability and accuracy caused by uncertainty, estimation errors and external disturbances in Eight-Rotor MAV, L2 gain control method was proposed based on interval type II fuzzy neural network identification here. In this control strategy, interval type II fuzzy neural network is used to estimate the uncertainty and non-linearity factor of the dynamic system, the adaptive variable structure controller is applied to compensate the estimation errors of interval type II fuzzy neural network, and at last, L2 gain control method is employed to suppress the effect produced by external disturbance on system, which is expected to possess robustness for the uncertainty and non-linearity. Finally, the validity of the L2 gain control method based on interval type II fuzzy neural network identifier applied to the Eight-Rotor MAV attitude system has been verified by three prototy experiments.

• Journal of Power System Engineering
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• v.2 no.2
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• pp.67-72
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• 1998

To improve control performance, especially positioning speed, of a pneumatic positioning system, dynamic characteristics of a control valve should be considered. In case we design controller including dynamic characteristics of a control valve, it's not easy to design controller gain using simple state feedback because degree of a control system is increased. This study designed controller using loop shaping of $H_{\infty}$ control theory for a model composed of a pneumatic actuator and a control valve, and positioning experiment using this controller was performed. As a result, it was verified that the controller is useful for high speed positioning of a pneumatic positioning system.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.3
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• pp.258-266
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• 2012

This paper describes a biped walking algorithm for a hydraulic humanoid robot on inclined floors. To realize stable and robust biped walking, the walking algorithm was divided into five control strategies. The first is a joint position control strategy. This strategy is for tracking desired joint position trajectories with a gain switching. The second is a multi-model based ZMP (Zero Moment Point) control strategy for dynamic balance. The third is a walking pattern flow control strategy for smooth transition from step to step. The fourth is an ankle compliance control, which increases the dynamic stability at the moment of floor contact. The last is an upright pose control strategy for robust walking on an inclined floor. All strategies are based on simple pendulum models and include practical sensory feedback in order to implement the strategies on a physical robot. Finally, the performance of the control strategies are evaluated and verified through dynamic simulations of a hydraulic humanoid on level and inclined floors.

• Journal of Power System Engineering
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• v.5 no.4
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• pp.90-96
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• 2001

In this study, a robust controller to control pressure in a pneumatic pressure vessel considering dynamic characteristics of pneumatic transmission line is proposed. Dynamic characteristics of transmission line using compressible fluid is changed by the flowing states of the fluid. So, if the fixed gain controller is designed based on a fixed model, the performance of the control system could be destabilized or degraded. The controller designed in this study is composed of two parts. The one is to reject modelling error based on the disturbance observer, the other is to obtain the control performance. The control results with the designed controller show that the robustness of the control system is achieved regardless of the change of the model of the transmission line. Therefore, the designed controller can be utilized for the performance improvement of the pressure control system using compressible fluid such as air and gas

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.44-52
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• 1994

In this paper, we assume that the dynamics of DC motor and nonlinear load are unknown. We propose an inverse dynamic model of DC motor and nonlinear load using the artificial neural network and construck speed control system based on the proposed dynamic model. We also propose another dynamic model with speed prediction scheme using the artificial neural network that removes the undesirable time delay effect caused by the computation time during the real-time control. We suggest a dynamic model which has arbitrary number of speed arguments and is especially effective when the motor and load has large moment of inertia. Next, we suggest a controller that combine the neurocontrol and PID control with constant gain. We show that the proposed neurocontrol systems have capabilities of noise rejection and generalization to have good velocity tracking through computer simulations and experiments.