• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1964-1966
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• 2001

Active Noise control(ANC) is rapidly becoming the most effective way to reduce noises that can otherwise be very difficult and expensive to control. This research presents ANFIS (Adaptive Network Fuzzy Inference System) controller for adaptively noise cancelling in a duct. ANC system generates secondary control sound pressure with same amplitude and with opposite phase as noise to be eliminated. ANFIS controller is trained to optimize its parameters for adaptively cancelling noise. That is ANFIS train its parameters by gradient descent and LSE method so called hybrid method. This paper present ANFIS in active noise control which provides an improvement convergence speed and limitation of linearity condition. It can model nonlinear functions of arbitrary complexity and ANFIS can construct an input-ouput mapping based on both human knowledge in the form of Takagi and Sugeno's fuzzy if-then rules and stipulated input-output data pairs. This paper also shows that the proposed ANFIS active noise control system successfully cancelled noise.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.1
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• pp.30-37
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• 2000

We studied on acquiring of transfer function and tuning of 2-DOF PID controller using ANFIS for the optimum control to turbine's variables variety. Since the shape of a membership function in the ANFIS based on the characteristics of plant. ANFIS based control method is effective for plant that its variable vary. On the other hand, a start-up time is very short and its variable's value for optimal start-up in gas turbine should be varied, but it is very difficult for such a controller to design. In this paper, we tune 2-DOF PID controller after apply a ANFIS to the operating data of Gun-san gas turbine and verify the characteristics. Its results is compared to the conventional PID controller and discuss. We expect this method will be used for another process because it is studied on the real operating data.

• Journal of Advanced Navigation Technology
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• v.16 no.4
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• pp.586-592
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• 2012

One approach of the control of a nonlinear system that has gained some success employs a fuzzy structure in cooperation with a neural network(ANFIS). The traditional ANFIS can only model and control the process in single-dimensional output nature in spite of multi-dimensional input. The membership function parameters are tuned using a combination of least squares estimation and back-propagation algorithm. In the case of a mobile robot, we need to drive left and right wheel respectively. In this paper, we proposed the control system architecture for a mobile robotic system that employs the 2-input 2-output ANFIS controller for trajectory tracking. Simulation results and preliminary evaluation show that the proposed architecture is a feasible one for mobile robotic systems.

• Journal of Mechanical Science and Technology
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• v.14 no.4
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• pp.418-425
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• 2000

Recently, many passenger cars have adopted automatic transmissions for shifting gears, and thus the smooth and precise control of gear shifts of passenger car automatic transmissions has become more and more essential for the riding comfort of vehicles equipped with automatic transmissions. In this article, a neural network-based supervisor for an automotive shift controller considering the throttle opening, variations in throttle opening, and the driving load is presented. For using the driving load information, an observer-based driving load estimation algorithm is proposed. A proportional-integral-derivative controller along with an open loop controller is used as a low level controller for controlling the gear shifts, and a supervisory controller for properly adapting the shift control parameters of the low level shift controller is designed using ANFIS. To evaluate the control performance of the proposed supervisor-based shift controller, both simulation studies and experimental studies are performed for various shifting situations.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.999-1004
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• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.958-961
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• 2001

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC is their robustness with respect to a model mismatch and disturbances. But it is difficult to apply for nonlinear systems. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in ANFIS can be effectively utilized to control a nonlinear systems. In this paper, we propose new ANFIS-based IMC controller for nonlinear systems. Numerical simulation results show that the proposed control scheme has good performances.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.2
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• pp.99-104
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• 2002

In this paper, we construct a hybrid intelligent controller based on a fusion scheme of GA(Genetic Algorithm) and FCM(Fuzzy C-Means) clustering-based ANFIS(Adaptive Neuro-Fuzzy Inference System). In the structure identification, a set of fuzzy rules are generated for a given criterion by FCM clustering algorithm. In the parameter identification, premise parameters are optimally searched by adaptive GA. On the other hand, consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. Finally, we applied the proposed method to the truck backer-upper control and obtained a better performance than previous works.

• Wind and Structures
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• v.22 no.6
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• pp.635-662
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• 2016

This paper deals with the comparison of different control strategies for the Induction generators in wind energy conversion system. Mainly, two types of induction machines, Self excited induction generator (SEIG) and doubly Fed Induction generators (DFIG) are studied. The different control strategies for SEIG and DFIG are compared. For SEIG, Electronic load Controller mechanism, Static Compensator based voltage regulator are studied. For DFIG the main control strategy namely vector control, direct torque control and direct power control are implemented. Apart from these control strategies for both SEIG and DFIG to improve the performance, the ANFIS based controller is introduced in both STATCOM and DTC methods. These control methods are simulated using MATLAB/SIMULINK and performances are analyzed and compared.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.144-150
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• 2002

In this paper a design of neuro-fuzzy-based predictive controller for nonlinear systems with time-delay is proposed. The proposed control system contains two neuro-fuzzy systems called ANFIS(Adaptive Neuro-Fuzzy Inference System). One is run as a series-parallel mode and the other is run as a parallel mode. An ANFIS running in series-parallel mode emulates the response of the nonlinear system with time-delay. Another ANFIS running in parallel mode generates the predicted output of the nonlinear system to compensate for the time-delays. Therefore, the proposed control system can be thought of as an extension of Smith-predictor scheme to the nonlinear systems with time-delay. A detailed design Procedure is presented and finally computer simulations are executed for the effectiveness of the proposed control scheme.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.5
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• pp.104-111
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• 2002

This paper presents a fuzzy rule extraction method using EM(Expectation-Maximization) algorithm and a design method of adaptive neuro-fuzzy control. EM algorithm is used to estimate a maximum likelihood of a GMM(Gaussian Mixture Model) and cluster centers. The estimated clusters is used to automatically construct the fuzzy rules and membership functions for ANFIS(Adaptive Neuro-Fuzzy Inference System). Finally, we applied the proposed method to the water temperature control system and obtained better results with respect to the number of rules and SAE(Sum of Absolute Error) than previous techniques such as conventional fuzzy controller.