• Journal of Information Processing Systems
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• v.18 no.4
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• pp.457-469
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• 2022

Hydraulic concrete buildings in the northwest of China are often subject to the combined effects of low-temperature frost damage, during drying and wetting cycles, and salt erosion, so the study of concrete deterioration prediction is of major importance. The prediction model of the relative dynamic elastic modulus (RDEM) of four different kinds of modified concrete under the special environment in the northwest of China was established using Grey residual Markov theory. Based on the available test data, modified values of the dynamic elastic modulus were obtained based on the Grey GM(1,1) model and the residual GM(1,1) model, combined with the Markov sign correction, and the dynamic elastic modulus of concrete was predicted. The computational analysis showed that the maximum relative error of the corrected dynamic elastic modulus was significantly reduced, from 1.599% to 0.270% for the BS2 group. The analysis error showed that the model was more adjusted to the concrete mixed with fly ash and mineral powder, and its calculation error was significantly lower than that of the rest of the groups. The analysis of the data for each group proved that the model could predict the loss of dynamic elastic modulus of the deterioration of the concrete effectively, as well as the number of cycles when the concrete reached the damaged state.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.28-28
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• 2000

Low-cost solid-state inertial sensors of three rate Gyroscopes and a triaxial Accelerometer are evaluated in static and dynamic environments. As a interim result, error models of each inertial sensors are generated. Model parameters with respect to temperature are acquired in static environment. These error models are included in an Extended Kalman Filter(EKF) to compensate bias error due to temperature variation. Experimental results in dynamic environment are included to show the validity of the each error model and the performance improvement of a compensated low cost inertial sensors for a navigational application

• Journal of Drive and Control
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• v.17 no.1
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• pp.44-50
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• 2020

For the requirement of accurate tracking control and the safety of physical human-robot interaction, torque control is basically desirable for humanoid robots. Because of the complexity of humanoid robot dynamics, the TDC (time-delay control) is practical because it does not require a dynamic model. However, there occurs a considerable error due to discontinuous non-linearities. To solve this problem, the TDC-FLC (fuzzy logic compensator) is applied to humanoid robots. The applied controller contains three factors: a TDE (time-delay estimation) factor, a desired error dynamic factor, and FLC to suppress the TDE error. The TDC-FLC is easy to execute because it does not require complicated humanoid dynamic calculations and the heuristic fuzzy control rules are intuitive. TDC-FLC is implemented on the whole body of a humanoid, not on biped legs even though it is performed by a virtual humanoid robot. The simulation results show the validity of the TDC-FLC for humanoid robots.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.530-533
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• 1993

This paper describes a new method of dynamic error compensation, using a digital convolution integrator and two digital low pass filters. In this method, the process of compensation consists of three steps. First, sampling and digitizing of input signal, second, removing the noise in sampled data by the low pass filter and third, making a convolution integral using the output data of low pass filters. This method showed a good experimental result of reducing dynamic error even if there was a slight noise in the input signal. As a result, the detecting time constant of resistance thermo-bulb was improved to about 1/10th.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.12
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• pp.57-64
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• 2007

There have been many researches for optimal controllers in multivariable systems, and they generally use accurate linear models of the plant dynamics. Real systems, however, contain nonlinearities and high-order dynamics that may be difficult to model using conventional techniques. Therefore, it is necessary a PID gain tuning method without explicit modeling for the multivariable plant dynamics. The PID tuning method utilizes the sign of Jacobian and gradient descent techniques to iteratively reduce the error-related objective function. This paper, especially, focuses on the role of I-controller when there is a steady state error. However, it is not easy to tune I-gain unlike P- and D-gain because I-controller is mainly operated in the steady state. Simulations for an overhead crane system with dynamic friction show that the proposed PID-LC algorithm improves controller performance, even in the steady state error.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.189-191
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• 2006

This paper analyzes effect of the spinning vehicle on the GPS signal. In rapid spinning vehicles such as missiles and space rockets, carrier phase and frequency depend on the roll rate of the vehicle. It induces phase and frequency modulation caused by the roll rate. The modulated phase and frequency increase dynamic stress error of the tracking loop. Even though higher order tracking loop can remove dynamic stress error, the dynamic stress error can not be remove in this case. In order to analyze the effect of the spinning vehicle on the GPS signal, the experiments are carried out. The experiment results show the modulation of the carrier frequency and phase caused by the roll rate of the spinning vehicle.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.286-286
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.154-161
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.6
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• pp.50-56
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• 1994

A DC generator control system was designed to control the torque of a rotating shaft precisely. The control system is composed of a strain gage type torque cell, a torque cell amplifier, a computer, a D/A converter, a error detector, a DC voltage amplifier and a resistor. The response test under unit step input and the dynamic stability test for the designed control system were carried out. It was confirmed that the settling time from the response test is about 4 s and the error from the dynamic stability test is less than 0.06% of rated output of torque cell. The designed control system may be used to control a DC generator which may be used to apply torque to a rotating shaft.

• Journal of IKEEE
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• v.14 no.2
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• pp.90-97
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• 2010

The high efficiency power management IC(PMIC) with DTMOS(Dynamic Threshold voltage MOSFET) switching device and DTMOS Error Amplifier is presented in this paper. PMIC is controlled with PWM control method in order to have high power efficiency at high current level. Dynamic Threshold voltage CMOS(DT-CMOS) with low on-resistance is designed to decrease conduction loss. The control parts in Buck converter, that is, PWM control circuits consist of a saw-tooth generator, a band-gap reference circuit, an DT-CMOS error amplifier and a comparator circuit as a block. the proposed DT-CMOS Error Amplifier has 72dB DC gain and 83.5deg phase margin. also Error Amplifier that use DTMOS more than CMOS showed power consumption decrease of about 30%. DC-DC converter, based on Voltage-mode PWM control circuits and low on-resistance switching device is achieved the high efficiency near 96% at 100mA output current. And DC-DC converter is designed with Low Drop Out regulator(LDO regulator) in stand-by mode which fewer than 1mA for high efficiency.