• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.67.2-67
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• 2001

In the adaptive fuzzy sliding mode control, from a set of fuzzy IF-THEN rules adaptive fuzzy sliding mode control whose parameters are adjusted on-line according to some adaptation laws is constructed for the purpose of controlling the plant to track a desired trajectory. Most of the research works in nonlinear controller design using fuzzy systems consider the affine system with fixed grid-rule structure based on system state availability. The fixed grid-rule structure makes the order of the controller big unnecessarily, hence the on-line fuzzy rule structure and fuzzy observer based adaptive fuzzy sliding mode controller is proposed to solve system state availability problems. Therefore adaptive laws of fuzzy parameters ...

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.4
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• pp.618-623
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• 2008

This paper presents a design method of the Interpolation-based adaptive LQ controller that is accomplished by getting the final controller interpolated with each gain of sub-LQ controllers. The Lagrange interpolation method is used in the scheme. The proposed controller is useful to control nonlinear systems which are especially changed the system parameters. The design method is illustrated by an application to the stabilization and tracking problems of an inverted pole system on a cart. Several cases of simulations are carried out in order to validate the control effectiveness and robustness. The simulation results are compared with those of LQ controller and prove the better control performance than LQ controller.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.21-28
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• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.647-655
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• 2001

In adaptive neuro-control, neural networks are used to approximate unknown plant nonlinearities. Until now, most of the studies in the field of controller design for nonlinear system using neural network considers the affine system with fixed number of neurons. This paper considers nonaffine nonlinear systems and on-line variation of the number of neurons. A control law and adaptive laws for neural network weights are established so that the whole system is stable in the sense of Lyapunov. In addition, at the expense of th input, tracking error converges to the arbitrary small neighborhood of the origin. The efficiency of the proposed scheme is shown through simulations ofa simple nonaffine nonlinear system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.315-322
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• 2004

A direct adaptive state-feedback controller for highly nonlinear systems is proposed. This paper considers uncertain or ill-defined nonaffine nonlinear systems and employs a static fuzzy logic system (FLS). The employed FLS estimates. and adaptively cancels an unknown plant nonlinearity using its proved universal approximation property. A control law and adaptive laws for unknown fuzzy parameters and bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov. The tracking error is guaranteed to be uniformly asymptotically stable rather than uniformly ultimately bounded with the aid of an additional robustifying control term. No a priori knowledge of an upper bound on an lumped uncertainty is required.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.7
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• pp.428-436
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• 2005

In this paper, we introduce an adaptive control method to improve the position accuracy and reduce nonlinear friction effects for the linear motion servo system with the nonlinear friction. The considered system plant included not only the variation of the mass of mover but also the friction change by the normal force. We also designed an adaptive controller with the mass estimator and the compensator by observing the variation of normal force. The effectiveness and system performances for the proposed control method in this paper show to improve than other control methods through numerical simulations.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.7
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• pp.31-41
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• 1993

In this paper, a fuzzy adaptive controller is proposed for the process with large delay time and unmodelled dynamics. The fuzzy adaptive controller consists of self tuning controller and fuzzy tuning part. The self tuning controller is designed with the continuous time GMV (generalized minimum variance) using emulator and weighted least square method. It is realized by the hybrid method. The controller has robust characteristics by adapting the inference rule in design parameters. The inference processing is tuned according to the operating point of the process having the nonlinear characteristics considering the practical application. We review the characteristics of the fuzzy adaptive controller through the simulation. The controller is applied to practical electric furnace. As a result, the fuzzy adaptive controller shows the better characteristics than the simple numeric self tuning controller and the PI controller.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1253-1256
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• 1996

In this paper, a new nonlinear feedback linearization control scheme for induction motors is developed. The control scheme employs a fuzzy nonlinear identification scheme based on fuzzy basis function expansion to adoptively compensate the parameter variations, i.e. rotor resistance, mutual and self inductance etc. An important feature of the proposed control scheme is to incorporate the sliding mode controller into the scheme to speed up convergence rate. Simulation tests show the robust behavior of the proposed controller in the presence of the parameter uncertainties of the machine.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.11
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• pp.922-933
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• 1989

This paper deals with an indirect pole placement adaptive controller design problem for discrete-time plants with arbitrary zeros in the presence of unmodeled dynamics and/or disturbances. The plant and controller parameters are estimated by separate estimators. The nonlinear feedback is introduced so that the estimated plant has as high degree of controllability as possible. The nonlinear feedback will be used in a finite time, after which the control algorithm becomes a standard pole placement one.