• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.207-213
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• 2000

This paper introduces a high-performance speed control system based on artificial neural networks(ANN) to estimate unknown parameters of a DC servo motor. The goal of this research is to keep the rotor speed of the DC servo motor to follow an arbitrary selected trajectory. In detail, the aim is to obtain accurate trajectory control of the speed, specially when the motor and load parameters are unknown. By using an artificial neural network, we can acquire unknown nonlinear dynamics of the motor and the load. A trained neural network identifier combined with a reference model can be used to achieve the trajectory control. The performance of the identification and the control algorithm are evaluated through the simulation and experiment of nonlinear dynamics of the motor and the load using a typical DC servo motor model.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.10
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• pp.1488-1497
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• 1989

In this paper, a new adaptive control scheme is proposed that uses a special form of rational function-type linear operator in the parameter adaptation and that removes the augmenting signal terms of the control input components. This adaptation scheme is applied to the MRAC of continuous-time, linear time-invariant, minimum-phase plants whose relative degrees are arbitrary. This scheme can be applied without any change of the controller structure to the adaptive systems regardless of the relative degree if it is greater than 1. And this scheme does not require any signal augmentation for arbitrary relative-degree plants if the reference model has no zeros. The asymptotic stability of the adaptive systems controlled by this scheme is shown by a hyper-stability method.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.961-970
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• 2014

This article deals with the problem of testing the equality of the scale parameters of several inverted exponential distributions. We propose Bayesian hypothesis testing procedures for the equality of the scale parameters under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the default Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. Simulation study and an example are provided.

• Proceedings of the KIEE Conference
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• 1999.07f
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• pp.2770-2772
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• 1999

This paper discusses the basic principle of vector control applied to induction motors. A indirect strategy for impressed stator currents control is used for system modeling. IRFOC for induction motor was modelled using "Matlab with Simulink" IRFOC be applied to 15kW induction motor is simulated in different conditions; step changes in torque at a fixed reference speed, and cyclic change of speed reference. Simulation result considering the variation of stator, rotor resistance includes the response of speed, torque and rotor d-q fluxes.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1299-1308
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• 2012

This article deals with the problem of testing the equality of the scale parameters in nonregular Pareto distributions.We propose Bayesian hypothesis testing procedures for the equality of the scale parameters under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be de ned up to a multiplicative constant. So we propose the default Bayesia hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. Simulation study and a real data example are provided.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.570-572
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• 2005

This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.74.3-74
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• 2001

This paper proposes a nonlinear adaptive controller based on back-stepping method for tracking reference substrate concentration by manipulating dilution rate in a continuous baker´s yeast cultivating process in stirred tank bioreactor. Control law is obtained from Lyapunov control function to ensure asymptotical stability of the system. The Haldane model for the specific growth rate depending on only substrate concentration is used in this paper. Due to the uncertainty of specific growth rate, it has been modified as a function including the unknown parameter with known bounded values. The substrate concentration in the bioreactor and feed line are measured. The deviation from the reference is observed when the external disturbance such as the change of the feed is introduced to the system ...

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.148.5-148
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• 2001

In this report, an adaptive flight control law based on a linear-parameter-varying (LPV) model is presented for a flight control system. The control system is designed to track an output of a vehicle to a reference signal from the guidance system, which generates a reference flight path. The proposed adaptive control law adjusts the controller gains continuously on line as flight conditions change. The obtained adaptive controller guarantees global stability over a wide flight envelope. Computer simulation involving six-degree-of-freedom nonlinear flight dynamics is applied to Japan´s automatic landing flight experimental vehicle (ALFLEX) to examine the effectiveness of the proposed adaptive flight control law.

• Journal of Mechanical Science and Technology
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• v.14 no.2
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• pp.168-176
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• 2000

Related to the error dynamics of an adaptive system, averaging theorems are developed for coupled differential equations which consist of ordinary differential equations and a parabolic partial differential equation. The results are then applied to the convergence analysis of the parameter estimate errors in the model reference adaptive control of a nonautonomous parabolic partial differential equation with lowly time-varying parameters.

• Journal of Power System Engineering
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• v.17 no.1
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• pp.77-84
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• 2013

The proportional pressure control valve that was used to relief valve has different dynamic characteristics on each case. Because this valve has different assembling or processing error and environmental condition. However, a customer who used the relief valve wants to have a steadily performance even if the dynamic characteristics of valve was changed. For this reason, the manufacturer try to make the robust controller that has simple structure. This paper concerns about the design of robust controller that didn't affected by plant parameter's changing. The control strategy is a model reference control that conducted by on line identification problem, gradient method and Lyapunov equation. This adaptvie control law's validity that this paper deal with was confirmed by an results of step response test or hysteresis test.