• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.49 no.5
• /
• pp.205-211
• /
• 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
• /
• 1991.07a
• /
• pp.708-712
• /
• 1991

This paper presents a new adaptive structure with fuzzy logic applied to adaptive controller. In the conventional adaptive control, good performance cannot be expected due to the adaptation gain of gradient algorithm fined as a constant. To change adaptation gain property, fuzzy rules, which are based on the output error and its rate of change, have been established. The proposed fuzzy adaptive law shows fast parameter convergence and improved performance. The fuzzy logic base is added to the conventional adaptive structure and little additional computation time is required.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.42 no.5 s.305
• /
• pp.13-18
• /
• 2005

Over the last few years, the control of bipedal robot has been considered a promising but difficult research field in the community of robotics. In this paper, a new robust output control method for a bipedal robot is proposed using the adaptive fuzzy logic. The adaptive fuzzy logic is used as an system approximator to cancel the unknown uncertainty. First, a model for a bipedal robot including switching leg influence, uncertainty and disturbance is presented. Second, a controller is designed in which the joint velocity measurement is not required. Fuzzy approximation error estimator is inserted in the system for tuning the fuzzy logic. Finally, the result of the computer simulation is presented to show the validity of the suggested control method.

• Proceedings of the KIEE Conference
• /
• 2002.07d
• /
• pp.2064-2066
• /
• 2002

This paper proposes Model Reference Adaptive Fuzzy System(MRAFS) using Fuzzy Logic Controller(FLC) as a adaptive laws in Model Reference Adaptive System(MRAS) in order to realize the speed-sensorless control of an induction motor. MRAFS estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. Fuzzy logic controller reduces the error of the rotor flux between the reference model and the adjustable model using the error and the change of error as the input of FLC. The computer simulation is executed to verify the propriety and the effectiveness of the proposed system.

• Proceedings of the KIEE Conference
• /
• 2006.07b
• /
• pp.1051-1052
• /
• 2006

This paper deals with mathmatical of an induction motor, considering non-linearity in the torque balance equation under closed loop operation with a reference speed. A controller based on Adaptive Nuro-Fuzzy Inference System (ANFIS) is developed to minimize overshoot and settling time following sudden changes in load torque. The overall system is modeled and simulated using the Matlab/simulink and Fuzzy Logic Toolbox. The advantages of fuzzy logic and neural network based fuzzy logic controller. Required training data the ANFIS controller is generated by simulation of the anti-windup PI controller is eliminated using the ANFIS controller. The transient deviation of the response from the set reference following variation in load torque is found to be negligibly samll along with a desirable reduction in settling time for the ANFIS controller.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.1929-1931
• /
• 2001

An adaptive fuzzy logic controller to regulate molten steel level in the strip casting process is presented, where parameters of fuzzy controllers are adapted stably by using Lyapunov-stability theory and a switching controller is used together to deal with the approximation error of fuzzy logic system. The level error is proven to converge to zero asymptotically. In the simulation, the clogging/unclogging of a stopper nozzle is considered and overcome by the proposed controller. Robustness to uncertainty is shown to be superior to conventional PI controller.

• Proceedings of the IEEK Conference
• /
• 2000.07b
• /
• pp.677-680
• /
• 2000

The technique of reinforcement learning algorithm is extended to solve the multiobjective control problem for uncertain dynamic systems. A multiobjective adaptive critic structure is proposed in order to realize a max-min method in the reinforcement learning process. Also, the proposed reinforcement learning technique is applied to a multiobjective satisfactory fuzzy logic controller design in which fuzzy logic subcontrollers are assumed to be derived from human experts. Some simulation results are given in order to show effectiveness of the proposed method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.23 no.11
• /
• pp.9-21
• /
• 2009

This paper proposes the alternative observer and controller design scheme based on T-S fuzzy model. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given unknown nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. The proposed controller is based on a simple output feedback method. Therefore, it solves the singularity problem, without any additional algorithm, which occurs in the inverse dynamics based on the feedback linearization method. The adaptive fuzzy scheme estimates the parameters and the feedback gain comprising the fuzzy model representing the observation system. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observer and controller, they are applied to an inverted pendulum on a cart.

• Journal of information and communication convergence engineering
• /
• v.3 no.1
• /
• pp.43-48
• /
• 2005

In this paper, the adaptive fuzzy logic controller(AFLC) is proposed, which uses real-coding genetic algorithm showing a good performance on convergence velocity and diversity of population among evolutionary computations. The effectiveness of the proposed AFLC was demonstrated by computer simulation for speed control system of AC servo motor. As a result of simulation for the AC servo motor, it is shown the proposed AFLC has the better performance on overshoot, settling time and rising time than the PI controller which is used when tuning AFLC.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.9 no.4
• /
• pp.309-314
• /
• 2009

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.