• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1834-1835
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• 2006

A robust adaptive position control algorithm is proposed for servomotor drive system with uncertainties and load disturbance. The proposed controller is comprised of a nominal controller and a robust control. The nominal controller is designed in the condition without all the external load disturbance, nonlinear friction and unpredicted uncertainties. The robust controller containing lumped uncertainty approximator using fuzzy-neural network(FNN) is designed to dispel the effect of uncertainties and load disturbance. The interconnection weight of the FNN can be online tuned in the sense of the Lyapunov stability theorem thus asymptotic stability of the proposed control system can be guaranteed. Finally, simulation results verify that the proposed control algorithm can achieve favorable tracking performance for the induction servomotor drive system.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.226-228
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• 2006

This paper is concerned with an Adaptive Fuzzy Sliding Mode Control(AFSMC) that the fuzzy systems are used to approximate the unknown functions of nonlinear system. In the adaptive fuzzy system, we adopt the adaptive law to approximate the dynamics of the nonlinear plant and to adjust the parameters of AFSMC. The stability of the suggested control system is proved via Lyapunov stability theorem, and convergence and robustness properties are demonstrated. The simulation results demonstrate that the performance is improved and the system also exhibits stability.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.199-203
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• 1997

In this paper is proposed a new direct adaptive fuzzy controller that dan ve applied for tracking control of a class of uncertain nonlinear SISO systems. It is shown that, in the presence of the perturbations such as fuzzy approximation error and external disturbance, boundedness of all the signals in the system is ensured, while under the assumption of no perturbations, the stability of the overall system in guaranteed. Also, the concept of persistent excitation in the adaptive fuzzy control systems is introduced to guarantee the convergence and the boundedness of adaptation parameter in the proposed controllers. Simulation example shows the effectiveness of the proposed method in the presence of fuzzy approximation error and external disturbance.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.5
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• pp.205-211
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• 2000

This paper presents an optimal design method for fuzzy logic controller (FLC) of HVDC using an Adaptive Evolutionary Algorithm(AEA). We have proposed the AEA which uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary algorithms. The AEA is used for tuning fuzzy membership functions and scaling constants. Simulation results show that disturbances are well damped and the dynamic performances of FLC have better responses than those of PD controller when AC system load changes suddenly.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.862-864
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• 1999

Adaptive Fuzzy control system is very powerful in nonlinear system, but That system require exactly membership function and parameter. If the membership function and parameter are not exact, the system will generate chattering. Using the Pole assignment compensator can remove the chattering and steepest descent method can reduce the convergence time. In this Paper, this algorithm applicate to the Inverted pendulum, so save proof of algorithm that is to be vigorous.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.531-533
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• 1999

The main aim of this paper is to investigate the possibility of applying fuzzy control algorithms to a microprocessor-based servomotor controller which requires faster and more accurate response compared with many other industrial processes. In addition, this study deals with the control of the lateral motion of a mobile vehicle. A adaptive fuzzy logic controller(AFLC) is designed and applied to a experimental mobile vehicle in order to achieve control of the lateral motion of the vehicle.

• 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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• 1999.07b
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• pp.773-775
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• 1999

In this paper, A new approach to stable adaptive fuzzy control of systems is proposed. The proposed scheme does not require an accurate mathematical model yet it guarantees an asymptotic stability. Fuzzy logic system, which has the property of universal approximator is used as an adaptive element of the proposed controller. Also this Paper proposes a fuzzy system that estimates the maximum limit of the uncertain term in the system dynamics to guarantee the Lyapunov stability. Proposed adaptive fuzzy control is applied to the DC servo motor system in order to show its good performance.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.161-163
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• 2005

This paper is concerned with an Adaptive Fuzzy Sliding Mode Control(AFSMC) that the fuzzy systems are used to approximate the unknown functions of nonlinear system. In the adaptive fuzzy system. we adopt the adaptive law to approximate the dynamics of the nonlinear plant and to adjust the parameters of AFSMC. The stability of the suggested control system is proved via Lyapunov stability theorem. and convergence and robustness properties are demonstrated. The simulation results demonstrate that the performance is improved and the system also exhibits stability.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1081-1082
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• 2008

This paper presents adaptive fuzzy control based on observer for nonlinear HVAC system whose states are not available. Fuzzy systems are employed to approximate the unknown nonlinear functions of the HVAC system and the state observer is designed for estimating the states of the HVAC system. An adaptive fuzzy controller is firstly constructed without the controller singularity problem. The obtained control system shows robustness and effectiveness compared with classical feedback controller. Simulation results are provided to illustrate the control performance.