• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.91-93
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• 2007

This paper presents multi-PI controller of IPMSM drive using fuzzy and neural-network. In general, PI controller in computer numerically controlled machine process fixed gain. To increase the robustness, fred gain PI controller, Multi-PI controller proposes a new method based fuzzy and neural-network. Multi-PI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.516-518
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• 1999

This paper presents an feed-forward neural network design instead PI controller for the speed control of an Induction Motor. The design employs the training strategy with Neural Network Controller(NNC) and Neural Network Emulator(NNE). Emulator identifies the motor by simulating the input and output map. In order to update the weights of the Controller. Emulator supplies the error path to the output stage of the controller using backpropagation algorithm. and then Controller produces an adequate output to the system due to neural networks learning capability. Therefore it becomes adjustable to the system with changing characteristics caused by a load. The speed control based on neural networks for induction motor is implemented by a vector controlled induction motor. The simulation results demonstrate that actual motor speed with neural network system well follows the reference speed minimizing the error and is available to implement on the vector control theory.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.83-86
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• 1995

This paper proposes an auto-tuning method of feedforward signal in boiler control of thermal power plants by using the neural network. The neural network produces an optimal feedforward signal by tuning the weights of the network. The weights are adapted effectively by using the teaching signal of PI control output. The proposed method was evaluated based on a detailed simulator which expressed non-linear characteristics of the 600 MW actual thermal power plant at load chaning operations, showed effectiveness in the learning of the weights of the neural network, and gave an accurate control performance in the temperature control of the system. Through the evaluation, the proposed method was proved to be effectively applicable to the actual thermal plants as the automatic adjustment tool.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.8
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• pp.68-74
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• 1998

The neural network predictiv econtroller (NNPC) is proposed for the attempt to mimic the function of brain that forecasts the future. It consists of two loops, one is for the prediction of output (NNP:neural network predictor) and the other one is for control the plant(NNC: neural network controller). The output of NNC makes the control input of plant, which is followed by the variation of both plant error and predictin error. The NNP forecasts the future output based upon the current control input and the estimated control output. The input and the output data of a system and a new method using evolution strategy are used to train the NNP. A two-step NNPC is applied to control the temeprature in boiler systems. It was compared with PI controller and auto-tuning PID controller. The computer simulaton and experimental results show that the proposed method has better performances than the other method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2499-2504
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• 2013

Applications of dynamic position control are especially focused on cancellation of unknown disturbance against nonlinear dynamic plants. Control performance is technically dependent upon observation methodology of such disturbance signals. This paper presents a novel control strategy by using linear actuators based on CAN communication networks. Disturbance is measured from placing a ball on a flat plant and image processing technique is applied to observe dynamic position of a ball system. We devise a neural network based PI control system to realize robust control of the dynamic system.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.282-284
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• 2006

Generally PI controller is used to control the speed of an induction motor. It has the good performance of speed control in case of adjusting the control parameters. But it occurred the problem to change the control parameters in the change of operation condition. In order to solve this problem, Fuzzy control or Artificial neural network is introduced in the speed control of an induction motor. However, Fuzzy control have the problems as the difficulties to change the membership function and fuzzy rule and the remaining error. Also Neural network has the problem as the difficulties to analyze the behavior of inner part. Therefore, the study on the combination of two controller is proceeded. In this paper, Speed controller of an induction motor based fuzzy-neural network is proposed and the speed control of an induction motor is performed using the proposed controller. Through the experiment, the fast response and good stability of the proposed speed controller is proved.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1740-1751
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• 2018

This paper proposes a novel fault tolerant tracking control (FTTC) scheme for a class of nonlinear systems with actuator failures based on the policy iteration (PI) algorithm and the adaptive fault observer. The estimated actuator failure from an adaptive fault observer is utilized to construct an improved performance index function that reflects the failure, regulation and control simultaneously. With the help of the proper performance index function, the FTTC problem can be transformed into an optimal control problem. The fault tolerant tracking controller is composed of the desired controller and the approximated optimal feedback one. The desired controller is developed to maintain the desired tracking performance at the steady-state, and the approximated optimal feedback controller is designed to stabilize the tracking error dynamics in an optimal manner. By establishing a critic neural network, the PI algorithm is utilized to solve the Hamilton-Jacobi-Bellman equation, and then the approximated optimal feedback controller can be derived. Based on Lyapunov technique, the uniform ultimate boundedness of the closed-loop system is proven. The proposed FTTC scheme is applied to reconfigurable manipulators with two degree of freedoms in order to test the effectiveness via numerical simulation.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.994-996
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• 2000

Switched reluctance motor generally operates in the magnetically saturated region because the saturation gives several benefits to its performance. This paper investigates the modelling and fuzzy tuning PI control of a nonlinear switched reluctance motor. The modelling is performed through neural network technique. Fuzzy auto-tuning PI control is designed for a robust performance in load and speed variations. Simulation and experimental results indicate better performances compared with simple PI control.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2089-2091
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• 2001

In this paper, a hybrid controller that consists of a conventional PD controller and a neural network controller which adapts to various control conditions by online learning is used and a new learning algorithm of the neural networks is used to prevent weights of neural network from diverging. A conventional PI controller and the hybrid controller is applied to speed control of 3 phase induction motor. So in comparison with a PD controller, we prove superiority of hybrid controller by experiments.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.5
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• pp.55-63
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• 2004

This paper presents neural network based controller using the feedback error loaming technique for a heavy load pointing system. Also the mathematical model was developed to analyze heavy load pointing system. The control scheme consists of a feedforward neural network controller and a fixed-gain feedback controller. This neural network controller is trained so as to make the output of the feedback controller zero. The proposed controller is compared with the conventional PI controller through simulations, and the results show that the pointing accuracy of the proposed control system are improved against the disturbance induced by vehicle running on the bump course.