• 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 KAIS Fall Conference
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• 2007.11a
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• pp.81-84
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• 2007

In this thesis, we have designed the indirect adaptive controller using Dynamic Neural Units(DNU) for unknown nonlinear systems. Proposed indirect adaptive controller using Dynamic Neural Unit based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our method is different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its training.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.68-75
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• 2004

In this paper, an estimator with an appropriate adaptive law for updating parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the parameterized plant model. Using the proposed estimator, the parameters of the T-S fuzzy model can be estimated by observing the behavior of the system and it can be a basis for indirect adaptive fuzzy control.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.1-22
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• 1997

Learning control refers to controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented a theory of indirect decentralized learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper extends these results to apply to the indirect repetitive control problem in which a periodic (i.e., repetitive) command is given to a control system. Decentralized indirect repetitive control algorithms are presented that have guaranteed convergence to zero tracking error under very general conditions. The original motivation of the repetitive control and learning control fields was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the desired trajectory. Decentralized repetitive control is natural for this application because the feedback control for link rotations is normally implemented in a decentralized manner, treating each link as if it is independent of the other links.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.62.4-62
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• 2002

$\textbullet$ Contents 1. Introduction $\textbullet$ Contents 2. System Description $\textbullet$ Contents 3. Decoupled Sliding Mde Control $\textbullet$ Contents 4. Decoupled Adaptive Fuzzy Sliding Mode Control through width adaptation $\textbullet$ Contents 5. Simulation Result $\textbullet$ Contents 6. Conclusion

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.720-729
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• 2005

In this paper, we design the indirect adaptive controller using Wavelet Neural Network(WNN) for unknown nonlinear systems. The proposed indirect adaptive controller using WNN consists of identification model and controller. Here, the WNN is used in both Identification model and controller The WNN has advantage of indicating the location in both time and frequency simultaneously, and has faster convergence than MLPN and RBFN. There are several training methods for WNN, such as GD, GA, DNA, etc. In this paper, we present the Extended Kalman Filter(EKF) based training method. Although it is computationally complex, this algorithm updates parameters consistent with previous data and usually converges in a few iterations. Finally, ore illustrate the effectiveness of our method through computer simulations for the Buffing system and the one-link rigid robot manipulator. From the simulation results, we show that the indirect adaptive controller using the EKF method has better performance than the GD method.

• 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.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.145-150
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• 2005

We proposed the indirect adaptive fuzzy model based sliding mode controller to control nonaffine nonlinear systems. Takagi-Sugano fuzzy system is used to represent the nonaffine nonlinear system and then inverted to design the controller at each sampling time. Also sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. The proposed controller and adaptive laws guarantee that the closed-loop system is stable in the sense of Lyapunov and the output tracks a desired trajectory asymptotically.