• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2283-2286
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• 2000

Sliding control is a powerful approach to controlling nonlinear and uncertain systems. Conventional sliding mode control suffer' from high control gain and chattering problem. also it needs mathematic! modeling equations for control systems. A Fuzzy controller is endowed with control rules and membership function that are constructed on the knowledge of expert, as like intuition and experience. but It is very difficult to obtain the exact values which are the membership function and consequent parameters. In this paper, without mathematical modeling equations, the plant parameters in sliding mode are estimated by the indirect adaptive fuzzy method. the proposed algorithm could analyze the system's stability and convergence behavior using Lyapunov theory. so sliding modes are reconstructed and decreased tracking error. moreover convergence time took a short. An example of inverted pendulum is given for demonstration of the robustness of proposed methodology.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.2
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• pp.133-145
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• 1996

This paper presents an indirect cutting force measuring system, which uses the current signals from the AC servo drive units of the horizontal machining center, with its applications to the adaptive regulation of the cutting forces in various milling processes and to the on-line monitoring of tool breakage. A typical model for the feed-drive control system of a horizontal machining center is developed to analyze cutting force measurement from the drive motor. The pulsating milling forces can be measured indirectly within the bandwidth of the current feedback control loop of the feed-drive system. It is shown that the indirectly measured cutting force signals can be used in the adaptive controller for cutting force regulation. The whole scheme has been embedded in the commercial machining center and a series of cutting experiments on the face cutting processes are performed. The adaptive controller reveals reliable cutting force regulating capability against the various cutting conditions. It is also shown that the tool breakage in milling can be detected within one spindle revolution by adaptively filtering the current signals. The effect of the cutter run-out has been considered for the reliable on-line detection of tool breakage.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.1
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• pp.51-65
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• 2002

This paper presents a design method of the neural network-based controller using an indirect adaptive control method to deal with an intelligent control for chaotic nonlinear systems. The proposed control method includes the identification and control Process for chaotic nonlinear systems. The identification process for chaotic nonlinear systems is an off-line process which utilizes the serial-parallel structure of multilayer neural networks and simple state space neural networks. The control process is an on-line process which uses the trained neural networks as the system model. An error back-propagation method was used for training of identification and control for chaotic nonlinear systems. The performance of the proposed neural network controller was evaluated by application to the Duffing equation and the Lorenz equation, and the proposed controller was compared with other neural network-based controllers by computer simulations.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.458-460
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• 1996

In this study, the full-state flux observer is designed in the synchronous DQ-frame and the speed adaptation rule is derived by using the MRAS(Model Reference Adaptive System) theory. In this rule, the induction motor becomes a reference model and the flux observer is taken as a adjustable model. A guideline of the adaptation gain is investigated for the precise and stable speed adaptation and the proposed scheme is compared with the conventional one designed in the stationary DQ-frame.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.724-727
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• 1999

In this paper, the direct and indirect neural adaptive controller are combined based on the Lyapunov synthesis approach. The proposed adaptive controller is constructed from RBF neural network and a set of fuzzy IF-THEN rules. And the weighting parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given trajectory. In this scheme, fuzzy IF-THEN rules are used to decide the combined weighting factor. It is shown that all the signals in the closed-loop system are uniformly bounded under mild assumptions. The effectiveness of the proposed control scheme is demonstrated through the control of one-link rigid robotics manipulator.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.319-322
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• 1992

This paper proposes indirect adaptive pole placement adaptive controller using a modified least squares method. If an adaptive controller has good performance, it is necessary that an estimator have fast convergence. This paper presents a modified least squares method which guarantees the stability of estimator and has fast convergence. In this algorithm, information on signal level is obtained from the determinent of covariance matrix and according to it, weighting factor is tuned.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.913-916
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• 1988

Indirect adaptive pole assignment PID controllers for unknown systems with time varying delay, is proposed. Unknown system parameters are estimated by recursive least square method, and time varying delay is estimated using indirect predictors. Since the order of parameter vectors didn't increase, the computational burden is not largely increased in spite of using indirect adaptive control method with time varying delay estimation. Computer simulation is performed to illustrate the efficiency of the proposed method.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2374-2376
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• 2000

In this paper, we propose an indirect vector control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) parameter estimator. It estimates the rotor time constant when the indirect vector control of induction motor is applied. We use the stator current error that is difference between the current command and estimated current calculated from terminal voltage and current. And two induced current estimate equations are used in training ANFIS.The estimator is trained by the hybrid learning algorithm. Simulation results shows good performance under load disturbance and motor parameter variations.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1182-1185
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• 1996

The objective of this paper is to control a nonlinear dynamical systems based on Self-Organized Distributed Networks (SODN). The learning with the SODN is fast and precise. Such properties are caused from the local learning mechanism Each local network learns only data in a subregion. Methods for indirect adaptive control of nonlinear systems using the SODN is presented. Through extensive simulation, the SODN is shown to be effective for adaptive control of nonlinear dynamic systems.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1037-1041
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• 1996

Input-output linearization technique in nonlinear control does not guarantee the robustness in the presence of parameter uncertainty or unmodeled dynamics, etc. However, it has been used as an important preliminary step in achieving additional control objectives, for instance, robustness to parameter uncertainty and disturbance attenuation. An indirect adaptive control scheme based on input-output linearization is proposed in this paper. The scheme consists of a Hopfield network for process parameter identification and an adaptive sliding mode controller based on input-output linearization, which steers the system response into a desired configuration. A numerical example is presented for the trajectory tracking of uncertain nonlinear dynamic systems with slowly time-varying parameters.