• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.45-49
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• 1997

In this paper, we controlled a Rotary Inverted Pendulum System using Neuro-Fuzzy Controller(NFC). The inverted pendulum system is widely used as a typical example of an unstable nonlinear control system which is difficult to control. Fuzzy theory have been because membership functions and rules of a fuzzy controller are often given by experts or a fuzzy logic control system. This controller is a feedforward multilayered network which integrates the basic elements and functions of a tradtional fuzzy logic controller into a connectionist structure which has distributed learning abilities. Such NFC can be constructed from training examples by learning rule, and the structure can be trained to develop fuzzy logic rules and find optimal input/output membership functions. Using this controller, we presented the results that controlled a Rotary Inverted Pendulum System and the associated algorithms.

• Journal of Power Electronics
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• v.5 no.1
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• pp.1-10
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• 2005

This paper presents the application of a simple neuro-based speed control scheme of a permanent magnet (PM) dc motor. To validate its efficiency, the performance characteristics of the proposed simple neuro-based scheme are compared with those of a Neural Network controller and those of a Fuzzy Logic controller under different operating conditions. The comparative results show that the simple neuro-based speed control scheme is robust, accurate and insensitive to load disturbances.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.231-242
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• 2023

A novel combinatorial type-2 adaptive neuro-fuzzy inference system (T2-ANFIS) and robust proportional integral derivative (PID) control framework for intelligent vibration mitigation of uncertain structural system is introduced. The fuzzy logic controllers (FLCs), are designed independently of the mathematical model of the system. The type-1 FLCs, have a limited ability to reduce the effect of uncertainty, due to their fuzzy sets with a crisp degree of membership. In real applications, the consequent part of the fuzzy rules is uncertain. The type-2 FLCs, are robust to the fuzzy rules and the process parameters due to the fuzzy degree of membership functions and footprint of uncertainty (FOU). The adaptivity of the proposed method is provided with the optimum tuning of the parameters using the neural network training algorithms. In our approach, the PID control force is obtained using the generalized type-2 neuro-fuzzy in such a way that the stability and robustness of the controller are guaranteed. The robust performance and stability of the presented framework are demonstrated in a numerical study for an eleven-story seismically-excited building structure combined with an active tuned mass damper (ATMD). The results indicate that the introduced type-2 neuro-fuzzy PID control scheme is effective to attenuate plant states in the presence of the structured and unstructured uncertainties, compared to the conventional, type-1 FLC, type-2 FLC, and type-1 neuro-fuzzy PID controllers.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.11a
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• pp.315-320
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• 2005

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of nor measured between the motor speed and output of a reference model. The control performance of the adaptive fuzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.24-29
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• 2005

In the fuzzy control for the multi-variable system, it is difficult to obtain the fuzzy rule. Therefore, the parallel structure of the independent single input-single output fuzzy controller using a pairing between the input and output variable is applied to the multi-variable system. However, among the input/output variables which arc not paired the interactive effects should be taken into account. these mutual coupling of variables affect the control performance. Therefore, for the control system with a strong coupling property, the control performance is sometimes lowered. In this paper, the effect of mutual coupling of variables is considered by the introduction of a neuro-fuzzy controller using relative gain matrix. This proposed neuro-fuzzy controller automatically adjusts the mutual coupling weight between variables using a neural network which is realized by back-propagation algorithm. The good performance of the proposed nero-fuzzy controller is verified through computer simulations on 200MW boiler systems.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.813-816
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• 1995

The shape and width of fuzzy membership function has an effect on performance of fuzzy controller. In this paper, neuro-fuzzy controller is proposed to improve the control performance of fuzzy controller. It has membership function, that is adapt to plant constant by using trained neural network. This neural network has been trained with back propagation algorithm. To show the effectiveness of proposed neuro-fuzzy controller with adaptive membership function, it is applied to plant (dead time + 1st order) with various plant constant.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.7
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• pp.9-15
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• 2012

This paper is proposed the adaptive fuzzy neuro controller(AFNC) for high performance of induction motor drive. The design of this algorithm based on the AFNC that is implemented using fuzzy controller(FC) and neural network(NN). This controller uses fuzzy rule as training patterns of a NN. Also, this controller adjusts the weights between the neurons of NN to minimize the error between the command output and the actual output using the back-propagation method. The control performance of the AFNC is evaluated by analysis in various operating conditions. The results of analysis prove that the proposed control system has high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Journal of Biosystems Engineering
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• v.28 no.2
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• pp.117-126
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• 2003

This study was conducted to develop precision control system fur cold storage facility that could offer safe storage environment for green grocery. For that reason of neuro-fuzzy control system with learning ability algorithm and single chip neuro-fuzzy micro controller was developed for cold storage facility. Dynamic characteristics and hunting of neuro-fuzzy control system were far superior to on-off and fuzzy control system. Dynamic characteristics of temperature were faster than on-off control system by 1,555 seconds(123% faster) and fuzzy control system by 460 seconds(36.4% faster). When system was arrived at steady state. hunting was ${\pm}$0.5$^{\circ}C$ in on-off control system, ${\pm}$0.4$^{\circ}C$ in fuzzy control system, and ${\pm}$0.3$^{\circ}C$ in neuro-fuzzy control system. Hunting of humidity and wind velocity was also controlled precisely by 70 to 72.5% and 1m/s For storage experiment with onion, characteristics of neuro-fuzzy control system were tested. Dynamic characteristics of neuro-fuzzy control system made cold storage facility conducted precooling ability and minimized hunting.

• Proceedings of the KIEE Conference
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• 2007.10c
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• pp.126-128
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• 2007

This paper proposes maximum torque control of IPMSM drive using Adaptive Fuzzy-Neuro controller and artificial neural network(ANN). The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. This paper proposes the analysis results to verify the effectiveness of the Adaptive Fuzzy-Neuro and ANN controller.