• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.671-676
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• 2007

In this paper, a novel neuro-fuzzy controller is presented. The generic fuzzy controller is compensated by a neural network controller so that an overall control structure forms a neuro-fuzzy controller. The proposed neuro-fuzzy controller solves the difficulty of selecting optimal fuzzy rules by providing the similar effect of modifying fuzzy rules simply by changing crisp input values. The performance of the proposed controller is tested by controlling humanoid robot arms. The humanoid robot arm is analyzed and implemented. Experimental studies have shown that the performance of the proposed controller is better than that of a PID controller and of a generic fuzzy PD controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.6
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• pp.252-257
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• 2002

We designed a neuro-fuzzy controller to improve some problems that are happened when the DC servo motor is controlled by a PID controller or a fuzzy logic controller. Our model proposed in this paper has the stable and accurate responses, and shortened settling time. To prove the capability of the neuro-fuzzy controller designed in this paper, the proposed controller is applied to the speed control of DC servo motor. The results showed that the proposed controller did not produce the overshoot, which happens when PID controller is used, and also it did not produce the steady state error when FLC is used. And also, it reduced the settling time about 10%. In addition, we could by aware that our model was only about 60% of the value of current peak of PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.401-405
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• 1993

This paper presents a neuro adaptive control method for nonlinear dynamical systems based on artificial neural network systems. The proposed neuro adaptive controller consists of 3 layers artificial neural network system and parallel PD controller. At the early stage in learning or identification process of the system characteristics the PD controller works mainly in order to compensate for the inadequacy of the learning process and then gradually the neuro contrller begins to work instead of the PD controller after the learning process has proceeded. From the simulation studies the neuro adaptive controller is seen to be robust and works effectively for nonlinear dynamical systems from a practical applicational points of view.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.423-430
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• 2004

A new active neuro-control strategy for seismic response reduction using modal states is proposed. In order to apply the neuro-control strategy to the given structural system it is needed to select state variables used as inputs into the neural network. If the degrees of freedom of the analytical model is large, there are so many possible combinations of the state variables. And selecting state variables is very complicated and troublesome task for the designer. In order to avoid this problem, the proposed control system adopts modal states as inputs. Since the modal states contain the information of the whole structural system's behavior, it is proper to use modal states as inputs of the neuro-controller. The simulation results show that the proposed the proposed active neuro-control strategy is quite effective to reduce seismic responses. In addition, the consuming time for training proposed neuro-controller is quite shorter than that for the conventional neuro- controller. The results of this investigation, therefore, indicate that the proposed active neuro-control strategy using modal states as the inputs could be effectively used for control seismically excited structures.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.70-70
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• 2000

Recently, a neuro-fuzzy approach, a combination of neural networks and fuzzy reasoning, has been playing an important role in the motor control. In this paper, a novel method of fiction compensation using neuro-fuzzy architecture has been shown to significantly improve the performance of a DC motor system with nonlinear friction characteristics. The structure of the controller is the neuro-fuzzy network with the TS(Takagi-Sugeno) model. A back-propagation neural network based on a gradient descent algorithm is employed, and all of its parameters can be on-line trained. The performance of the proposed controller is compared with both a conventional neuro-controller and a PI controller.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.474-476
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• 1998

In this paper, a multi variable neuro-fuzzy controller for a boiler-turbine system is designed. Two architectures are used. The first consists of boiler-turbine system identification and the second is designing a controller. A generalized backpropagation algorithm is developed and used to train the neuro-fuzzy controller. Designed controller is good tracking property and rejects the input and output disturbances. The results of the proposed design method is verified through simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.956-959
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• 1996

In this study, we propose a neuro-genetic controller combined with a linear controller in parallel to improve the tracking performance of the Line of Sight(LOS) stabilization system and reject the effect of disturbances. A Genetic Algorithm(GA) is used to optimize weights of the neuro-genetic controller since this algorithm can search a global minimum without derivatives or other auxiliary knowledge. The LOS system is very complex and has limited measurable output data. Under these specific circumstances GA solves many problems that other training methods have. Computer simulation results show that the, proposed controller makes better tracking response and rejection of disturbance than a linear controller.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.1
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• pp.52-59
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• 1998

This paper represents identification and control designs using neural networks for a class of nonlinear dynamic systems. A proposed neuro-controller is a combination of a linear controller and a neural network, and is trained by indirect neuro-control scheme. The proposed neuro-controller is implemented and tested on an IBM PC-based bar system, and is applicable to many dc-motor-driven precisiion servo mechanisms. The ideas, algorithm, and experimental results are described. Experimental resutls are shown to be superior to those of conventional control.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.855-858
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• 1995

A neural network has very simple construction (input, output and connection weight) and then it can be robusted against some disturbance. In this paper, we proposed a neuro-controller using a Multi-Layered neural network which is combined with PD controller. The proposed neuro-controller is learned by backpropagation learning rule with momentum and neuro-controller adjusts connection weight in neural network to make approximate dynamic model of DC Servo motor. Computer Simulation results show that the proposed neuro-controller's performance is better than that of origianl PD controller.

• The Transactions of the Korean Institute of Power Electronics
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• v.16 no.5
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• pp.484-493
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• 2011

The MPPT algorithm using neuro-fuzzy controller is proposed to improve the performance of fuzzy controller in this paper. The width of membership function and fuzzy rule have an effect on the performance of fuzzy controller. The neuro-fuzzy controller has the response characteristic which is superior to the existing fuzzy controller, because of using the optimal width of the fuzzy membership function through the neural learning. The superior control characteristic of a proposed algorithm is confirmed through simulation and experiment results.