• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.5
• /
• pp.481-486
• /
• 2002

In this paper, a new on-line parameter estimation methodology for the general continuous time Takagi-Sugeno(T-5) fuzzy model whose parameters are poorly known or uncertain is presented. An estimator with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the plant parameterized model. By 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 the indirect adaptive fuzzy control. Based on the derived design method, the parameter estimation for controllable canonical T-S fuzzy model is also Presented.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.40.5-40
• /
• 2001

In this paper, a new on-line parameter estimation methodology for the general continuous time Takagi-Sugeno(T-S) fuzzy model whose parameters are poorly known or uncertain is presented. An estimator with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the plant parameterized model. By 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 the indirect adaptive fuzzy control.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.14 no.3
• /
• pp.513-525
• /
• 1990

This paper presents Adaptive Robust Servocontrol(ARSC) scheme, which is an explicit(or indirect) pole-assignment adaptive algorithm with the property of "robustness". It guarantees asymptotic regulation and tracking in the presence of finite parameter perturbations of the unknown plant(or process) model. The controller structure is obtained by transforming a robust control theory into an adaptive control version. This controller structure is combined with the model estimation algorithm which includes a dead-zone for bounded noise. It is proved theoretically that this combination of control and identification is globally convergent and stable. It is also shown, through a real-time simulation study, that the desired closed-loop poles of the augmented system can be assigned directly, and that the adjustment mechanism of the scheme tunes the controller parameters according to the assigned closed-loop poles.oop poles.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.1716-1717
• /
• 2007

In this paper, a second order fuzzy sliding mode control that combines with a adaptive self-regulating technique is proposed for a nonlinear system with unknown dynamics. The chattering effect that is a representative disadvantage of the sliding mode control is avoided by using the second order sliding mode control instead of the first order sliding mode control. The proposed sub-controller is composed of the equivalent control that is approximated by an online rule regulation sheme and the hitting control that is used to constrain the states of the sub-system to maintain on the sub-sliding surface and used to guarantee the system robustness. Simulation results are presented to show the effectiveness of the proposed controller

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.791-794
• /
• 1995

This paper introduces a new approach to adaptive control using a combination of direct, indirect and variable structrure method. A new variable structrue input is derived to counteract the effects of disturbances. Direct input is used to have fast response and indirect input improves the transient behavior of the system. Computer simulation results illustrate the very satisfactory performance of the proposed algorithm.

• Proceedings of the KIEE Conference
• /
• 1987.11a
• /
• pp.73-75
• /
• 1987

The paper proposes a class of discrete-time adaptive controller which may be applicable without sufficient a priori information. Against choices of the Information, GPC algorithm may seem to be more robust than any other methods reported, but it is the method based on Indirect approach. It is, therefore, reasonable to propose an algorithm via the zero-gain prediction, in which the control parameters are directly estimated and calculated.

• 제어로봇시스템학회:학술대회논문집
• /
• 1986.10a
• /
• pp.352-355
• /
• 1986

In this paper, some candidates suggested for the improvement of the robustness in adaptive control systems are shortly surveyed. Using dead zone concept of error in adaptation process, gain retardation methods such as .sigma.-modification and parameter restriction method are those considered. Feedforward compensation and normalized adaptation technique are also considered. New modeling technique suggested by Donati et al is used for the indirect control of plants containing unmodeled dynamics. The frequency band of input signal, which is used as a test signal and control signal simultaneously, is directly related to the control of plants containing high frequency parastics. Computer simulation results of the some selected algorithms are shown.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.162.5-162
• /
• 2001

This paper presents a indirect vector control system for induction motors using an adaptive fuzzy logic(AFL) speed estimator. The proposed speed estimator is based on the MRAS(Mode Referece Adaptive System) scheme. In general, the MRAS speed estimation approaches are more simple than any other strategies. However, there are some difficulties in the scheme, which are strong sensitivity to the motor parameters variations and necessity to detune the estimator gains caused by different speed area. In this paper, the AFL speed estimator is proposed to solve the problems. The structure of the proposed AFL is very simple. The input of the AFL is the rotor flux error difference between reference and adjustable model, and the output is the estimated incremental rotor speed. Moreover, the back propagation algorithm is combined to adjust the parameters of the fuzzy logic to the most appropriate values during the operating the system. Finally, the validity of the ...

• Journal of Institute of Control, Robotics and Systems
• /
• v.7 no.2
• /
• pp.125-130
• /
• 2001

This paper presents an indirect field-oriented (IFO) induction motor position servo drives which uses the model following adaptive controller with the artificial neural network(ANN)-based rotor resistance estimator. The model reference adaptive system(MRAS)-based 2-layer ANN estimates the rotor resistance on-line and a linear model-following position controller is designed by using the estimated the rotor resistance value. At the end, a fuzzy logic system(FLS) is added to make the position controller robust to the external disturbances and the parameter variations. The simulation results show the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
• /
• 2002.11c
• /
• pp.71-74
• /
• 2002