• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.11
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• pp.476-482
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• 2006

Based on a field-oriented model of induction motor, an adaptive backstepping control approach using neural networks is proposed in this paper for the speed control of induction motors with uncertainties at a minimum of information. Neural networks are used to approximate most of uncertainties which are derived from unknown motor parameters, load torque disturbances and unknown nonlinearities and an adaptive backstepping controller is used to derive adaptive law of neural networks and control input directly. The controller is implemented by the hardware using DSP and the effectiveness of the proposed approach is verified by carrying out the experiment.

• Journal of Navigation and Port Research
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• v.30 no.2
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• pp.119-124
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• 2006

In Part I(theoretical study) of the paper, a new adaptive autopilot for ships based on Adaptive Neural Networks was proposed. The ANNAI autopilot was designed for course-keeping, turning and track-keeping control for ships. In this part of the paper, to show the effectiveness and feasibility of the ANNAI autopilot and automatic selection algorithm for learning rate and number of iterations, computer simulations of course-keeping and track-keeping tasks with and without the effects of measurement noise and external disturbances are presented. Additionally, the results of the previous studies using Adaptive Neural Network by backpropagation algorithm are also showed for comparison.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2005.10a
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• pp.23-28
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• 2005

In Part I (theoretical study) of the paper, a new adaptive autopilot for ships based on Adaptive Neural Networks was proposed. The ANNAI autopilot was designed for course-keeping, turning and track-keeping control for ships. In this part of the paper, to show the effectiveness and feasibility of the ANNAI autopilot, computer simulations of course-keeping and track-keeping tasks with and without the effects of measurement noise and external disturbances are presented. Additionally, the results of the previous studies using Adaptive Neural Network by backpropagation algorithm are also showed for comparison.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1226-1231
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• 2008

A design of attitude controller for a tiltrotor is presented augmenting L1 adaptive control, neural networks, and feedback linearization. The neural networks compensate for the modeling error caused by the lack of knowledge of tiltrotor dynamics while the L1 adaptive control allows high adaptation gains in adaptation laws thereby, satisfying tracking performance requirement. The efficacy of this control methodology is illustrated in high-fidelity nonlinear simulation of a tiltrotor by flying the tiltrotor in different flight modes from where the L1 adaptive controller with neural networks is originally designed for.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1070-1078
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• 2000

A nonlinear adaptive flight control system is proposed using a backstepping controller with neural network controller. The backstepping controller is used to stabilize all state variables simultaneously without the two-timescale assumption that separates the fast dynamics, involving the angular rates of the aircraft, from the slow dynamics which includes angle of attack, sideslip angle, and bank angle. It is assumed that the aerodynamic coefficients include uncertainty, and an adaptive controller based on neural networks is used to compensate for the effect of the aerodynamic modeling error. It is shown by the Lyapunov stability theorem that the tracking errors and the weights of neural networks exponentially converge to a compact set. Finally, nonlinear six-degree-of-freedom simulation results for an F-16 aircraft model are presented to demonstrate the effectiveness of the proposed control law.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.14-19
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• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.06b
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• pp.67-74
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• 2006

In this paper, an adaptive neural network controller and its application to automatic berthing control of ship is presented. The neural network controller is trained online using adaptive interaction technique without any teaching data and off-line training phase. Firstly, the neural networks used to control rudder and propeller during automatic berthing process are presented. Finally, computer simulations of automatic ship berthing are carried out to verify the proposed controller with and without the influence of wind disturbance and measurement noise.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.5
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• pp.32-43
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive learning fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive learning fuzzy neural network and artificial neural network.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.903-905
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• 2003

In this paper, adaptive high-order neural network controller(AHONNC) is adopted to control of an induction servomotor. A algorithm is developed by combining compensation control and high-order neural networks. Moreover, an adaptive bound estimation algorithm was proposed to estimate the bound of approximation error. The weight of the high-order neural network can be online tuned in the sense of the Lyapunov stability theorem; thus, the stability of the closed-loop system can be guaranteed. Simulation results for induction servomotor drive system are shown to confirm the validity of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.316-321
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• 2003

This paper presents a design method of the wavelet neural network based controller using direct adaptive control method to deal with a stable intelligent control of chaotic systems. The various uncertainties, such as mechanical parametric variation, external disturbance, and unstructured uncertainty influence the control performance. However, the conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on direct adaptive control method with adaptive learning rates is proposed to control chaotic nonlinear systems whose mathematical models are not available. The adaptive learning rates are derived in the sense of discrete-type Lyapunov stability theorem, so that the convergence of the tracking error can be guaranteed in the closed-loop system. In the whole design process, the strict constrained conditions and prior knowledge of the controlled plant are not necessary due to the powerful learning ability of the proposed intelligent control system. The gradient-descent method is used for training a wavelet neural network controller of chaotic systems. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with application to the chaotic systems.