• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.6
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• pp.1122-1127
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• 2001

This paper proposed a speed adaptive control system with load torque observer and feed-forward compensation using neural network for air compressor system driven an induction motor. The motor receive impact load change under the influence of piston movement of up and down, and so it difficult to obtain good speed control characteristics. With real-time adjusting control gain estimated in neural network, control characteristics of motor is improved. The validity of the proposed system is confirmed through the theoretical analysis and computer simulation.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.409-412
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• 2000

This paper investigates a novel speed identification of induction motor using 2 layer neural networks. The proposed control strategy is based on neural networks using model of full order state observer. in the proposed neural networks system the error between the desired variable and the adaptive variable is back-propagated to adjust the rotor speed, So that the adaptive variable will coincide with the desired variable. The proposed control algorithm is verified through simulation and experiment using th digital signal processor of TMS320C31

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1646_1647
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• 2009

In this paper, Takagi-Sugeno(T-S) fuzzy controller and search method are developed for efficiency optimization of induction motors(IMs). The proposed control scheme consists of efficiency controller and adaptive backstepping controller. A search controller for which information of input of T-S fuzzy controller is included in efficiency controller that uses a direct vector controlled induction motor. A sliding mode observer is designed to estimate rotor flux and an adaptive backstepping controller is used to control of speed of IMs. Simulation results are presented to validate the proposed controller.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.296-298
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• 1993

The performance of MODEL-BASED controller is influenced by model parameter errors and velocity measurement errors. To reduce this errors, Control methods by parameter adaptation and velocity estimation are studied. But because these controller has complex construction and need much computation time, the implementation of single processor system is difficult. This paper proposes a control scheme which combines an adaptive control law with a sliding observer using transputer network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.397-400
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• 2001

This paper proposed a speed adaptive control system with load torque observer and feed-forward compensation using neural network for air compressor system driven an induction motor. The motor receive impact load change under the influence of piston movement of up and down, and so it difficult to obtain good speed control characteristics. With real-time adjusting control gain estimated in neural network, control characteristics of motor is improved. The validity of the proposed system is confirmed through the theoretical analysis and computer simulation.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.2
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• pp.82-88
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• 2005

A compensation scheme for a class of linearly parameterized systems is presented. The compensator consists of a typical linearizing control and an adaptive observer with integration type update law, which is based on Speed Gradient (SG) algorithm.. Instead of the intermediate functions of the compensation schemes suggested by other researchers, the proposed compensator is designed with some design functions which guarantee the growth, convexity, attainability, and pseudo gradient conditions in the update law. The scheme achieves the asymptotic stability of the tracking error and the boundedness of the estimation errors. A numerical example is given to demonstrate the validity of the proposed design.

• Journal of Power Electronics
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• v.16 no.2
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• pp.598-609
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• 2016

The good control performance of permanent magnet linear synchronous motor (LSM) drive systems is difficult to achieve using linear controllers because of uncertainty effects, such as fictitious forces. A backstepping control system using adaptive modified recurrent Laguerre orthogonal polynomial neural network uncertainty observer (OPNNUO) is proposed to increase the robustness of LSM drive systems. First, a field-oriented mechanism is applied to formulate a dynamic equation for an LSM drive system. Second, a backstepping approach is proposed to control the motion of the LSM drive system. With the proposed backstepping control system, the mover position of the LSM drive achieves good transient control performance and robustness. As the LSM drive system is prone to nonlinear and time-varying uncertainties, an adaptive modified recurrent Laguerre OPNNUO is proposed to estimate lumped uncertainties and thereby enhance the robustness of the LSM drive system. The on-line parameter training methodology of the modified recurrent Laguerre OPNN is based on the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Laguerre OPNN are derived to accelerate parameter convergence. Finally, the effectiveness of the proposed control system is verified by experimental results.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.2
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• pp.125-131
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• 2017

2-Axis Pan and Tilt Motion Platform, a complex multivariate non-linear system, may incur any disturbance, thus requiring system controller with robustness against various disturbances. In this study, we designed an adaptive backstepping compensated controller by estimating the disturbance and error using the Radial Basis Function Neural Network (RBF NN). In this process, Uniformly Ultimately Bounded (UUB) was demonstrated via Lyapunov and stability was confirmed. By generating progressive disturbance to the irregular frequency and amplitude changes, it was verified for various environmental disturbances. In addition, by setting the RBF NN input vector to the minimum, the estimated disturbance compensation process was analyzed. Only two input vectors facilitated compensatory function of RBF NN via estimating the modeling and control error values as well as irregular disturbance; the application of the process resulted in improved backstepping controller performance that was confirmed through simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.174-180
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• 2012

This paper proposes a decentralized adaptive output-feedback controller design for nonlinear interconnected systems with unknown time delays. The interaction terms with unknown delays are related to all states of subsystems. The time-delayed functions are compensated by using appropriate Lyapunov-Krasovskii functionals and function approximation technique. The observer dynamic surface design technique is employed to design the proposed memoryless local controller for each subsystem. In addition, we prove that all signals in the closed-loop system are semiglobally uniformly bounded and control errors converge to an adjustable neighborhood of the origin.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.293-294
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• 2007

This paper addresses the adaptive controller for efficiency optimization of induction motors. The paper describes an adaptive controller based on-line efficiency optimization control of a drive that uses a direct vector controlled induction motors. To improve the efficiency of the induction motors, it is important to find the optimal flux reference that minimize power loss. The proposed optimal flux reference is derived using a power loss function that is constructed with stator resistance losses, rotor resistance losses and core losses. The proposed sliding mode flux observer generates estimates the unmeasured rotor fluxes. An optimal efficiency controller has goal of maximizing the efficiency for a given speed and load torque. A simulation shows the effectiveness of the proposed technique.