• Journal of Institute of Control, Robotics and Systems
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• v.9 no.12
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• pp.1019-1025
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• 2003

We introduce a new auto-calibration/simulation method for a strain gage type transducer/signal-conditioner which guarantees the output linearity and compensates the error automatically. We design a micro voltage supply which is able to interface either AC or DC type excitation voltage. A new strain gage simulator is also designed. We make linearity output of the signal conditioner and can compensate error automatically with this new auto calibration/simulation method. The experimental results show that the error between the real value and the expected one is less than 1%.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.72-76
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• 1990

A new statistical linearization technique for nonlinear system called covariance matching method is proposed in this paper. The covariance matching method makes the mean and variance of an approximated output be identical real functional output, and the distribution of the approximated output have identical shape with a given random input. Also, the covariance matching method can be easily implemented for statistical analysis of nonlinear systems with a combination of linear system covariance analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.124-131
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• 1997

This paper presents a modified adaptive control scheme that improves the transient performance of the overall system while maintaining the asymptotic convergence of the output error. The proposed control scheme is characterized as the added outer dynamic feedback loop on the conventional adaptive control scheme. This control scheme enables various robust control methods that were developed for standard model reference adaptive controllers to be applied to the proposed controller. In contrast with the modified adaptive controllers that use augmented errors to provide additional dynamic feedback, the proposed controller uses tracking error directly, thereby reducing the tracking error significantly in the transient state and making the error insensitive to noise.

• Journal of Power Electronics
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• v.18 no.2
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• pp.533-546
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• 2018

This study proposes a comprehensive mathematical model that includes coil-system circuit and loss models for power converters in wireless power transfer (WPT) systems. The proposed model helps in understanding the performance of WPT systems in terms of coil-to-coil efficiency, overall efficiency, and output power capacity and facilitates system performance optimization. Three methods to achieve constant output power for variable-load systems are presented based on system performance analysis. An optimal method can be selected for a specific WPT system by comparing the efficiencies of the three methods calculated with the proposed model. A two-coil 1 kW WPT system is built to verify the proposed mathematical model and constant output power control methods. Experimental results show that when the load resistance varies between 5 and $25{\Omega}$, the system output power can be maintained at 1 kW with a maximum error of 6.75% and an average error of 4%. Coil-to-coil and overall efficiencies can be maintained at above 90% and 85%, respectively, with the selected optimal control method.

• Structural Engineering and Mechanics
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• v.12 no.6
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• pp.633-656
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• 2001

In this paper, modal control with direct output feedback is formulated in a systematic manner for easy implementation. Its application to the seismic protection of structural systems is verified by a shaking table test, which involves a full-scale building model and an active bracing system as the control device. Two modal control cases, namely, one full-state feedback and one direct output feedback control were tested and compared. The experimental result shows that in mitigating the seismic response of building structures, modal control with direct output feedback can be as effective and efficient as that with full-state feedback control. For practical concerns, the control performance of the proposed method in the presence of sensor noise and stiffness modeling error was also investigated. The numerical result shows that although the control force may be increased, the maximum floor displacements of the controlled structure are very insensitive to sensor noise and modeling error.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.2
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• pp.399-407
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• 1996

A fuzzy learning algorithm to get the optimal fuzzy rules is presented in this paper. The algorithm introduces a reference model to generate a desired output and a performance index funtion instead of the performance index table. The performance index funtion is a cost function based on the error and error-rate between the reference and plant output. The cost function is minimized by a gradient method and the control input is also updated. In this case, the control rules which generate the desired response can be obtained by changing the portion of the error-rate in the cost funtion. In SISO(Single-Input Single- Output)plant, only by the learning delay, it is possible to experss the plant model and to get the desired control rules. In the long run, this algorithm gives us the good control rules with a minimal amount of prior informaiton about the environment.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.1
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• pp.8-14
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• 2005

This paper is proposed hybrid artificial intelligent(HAI) controller for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on HAI 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 HAI controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.9
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• pp.569-575
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for high performance of induction motor drive. The design of this algorithm based on 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 analysis prove that the proposed control system has strong high performance and robustness to parameter variation. and steady- state accuracy and transient response.

• 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.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.8
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• pp.1167-1177
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• 1989

In this paper, a generalized adaptive law is proposed which uses a rational function type operator for parameter adjustment. To satisfy the passivity condition of the adaptation block, we introduce a constant feedback gain into the adaptation block. This adaptation scheme is applied to the model reference adaptive control of a continuous-time, linear time-invariant, minimum-phase system whose relative degree is 1. We prove the asymptotic stability of the output error of this adaptive system by hyperstability method. It is shown that by digital computer simulations this law can give a better output error transient response in some cases than the conventional gradient adaptive law. And the output error responses for the several types of the proposed adaptation law are examined in the presence of a kind of unmodeled dynamics.