• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2004.07b
• /
• pp.1234-1238
• /
• 2004

A Controlled neural networks are proposed in order to measure nonlinear environments in adaptive and in realtime. The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between tile output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we have various experiments. And this controller call prove effectively to be control in the environments of various systems.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.9 no.5
• /
• pp.463-471
• /
• 1999

This paper presents a vector controlled induction motor is implemented by neural networks system compared with PI controller for the speed control. The design employed the training strategy with Neural Network Controller(NNC) and Neural Network Emulator(NNE) for speed. In order to update the weights of the controller First of all Emulator updates its parameters by identifying the motor input and output next it supplies the error path to the output stage of the controller using backpropagation algorithm, As Controller produces an adequate output to the system due to neural networks learning capability Vector controlled induction motor characteristics actual motor speed with based on neural network system follows the reference speed better than that of linear PI speed controller.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.45 no.1
• /
• pp.42-53
• /
• 1996

The paper presents a speed control system of vector controlled induct- ion motor using neural networks. The main feature of proposed speed control system is a Neural Network Controller(NNC) which supplies torque current to induction motor and Neural Network Emulator(NNE) which captures the forward dynamics of induction motor. A back propagation training algorithm is employed to train the NNE and NNC. In order to determine the NNC output error, plant(induction motor) output error can be back propagated through the NNE. The NNC and NNE for speed control of vector controlled induction motor is carried out by TMS320C30 DSP and IGBT current regulated PWM inverter. Through computer simulation and experimental results, it is verified that proposed speed control system is robust to the load variation. (author). refs., figs.

• Journal of Sensor Science and Technology
• /
• v.12 no.1
• /
• pp.51-56
• /
• 2003

We describe a charge pump circuit using VCO (voltage controlled oscillator) for storing information into local memories in neural networks. The VCO is used for adjusting the output voltage of the charge pump to the reference voltage and for reducing the fluctuation generated by the clocking scheme. The charge pump circuit is simulated by using Hynix 0.35um CMOS process parameters. The proposed charge pump operates properly regardless to the temperature and the supply voltage variation.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.3
• /
• pp.263-272
• /
• 2000

In this paper we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastcic approximation method it is regarded as a stochastic recursive filter algorithm. In addition we provide a filtering and control condition for a stochastic nonlinear system called the perfect filtering condition in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable and the proposed neural controller is more efficient than the conventional LQG controller and the canonical LQ-Neural controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.346-352
• /
• 1998

In this paper, we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastic approximation method, it is regarded as a stochastic recursive filter algorithm. In addition, we provide a filtering and control condition for a stochastic nonlinear system, called perfect filtering condition, in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence, the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable. and the proposed neural controller is more efficient than the conventional LQG controller and the canoni al LQ-Neural controller.

• Journal of the Korean Institute of Navigation
• /
• v.21 no.1
• /
• pp.109-118
• /
• 1997

In this paper, applications of multilayer neural networks to control of flexible robot beam are considered. The multilayer nerual networks can be used to approximate any continuous function to a desired degree of accuracy and the weights are updated by Gradient Method. When a flexible beam is rotated by a motor through the fixed end, transverse vibration may occur. The motor torque should be controlled insuch a way that the motor rotates by a specified angle, while simultaneously stabilizing vibration of the flexible manipulators so that is arrested as soon as possbile at the end of rotation. Accurate control of lightweight beam during the large changes in configuration common to robotic tasks requires dynamic models that describe both rigid body motions, as well as the flexural vibrations. Therefore, a linear dynamic state-space model of for a single link flexible robot beam is derived and PD controller, LQP controller, and inverse dynamical neural networks controller are composed. The effectiveness the proposed control system is confirmed by computer simulation.

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

• International Journal of Control, Automation, and Systems
• /
• v.2 no.1
• /
• pp.92-99
• /
• 2004

In this paper, the decoupled neural network reference compensation technique (DRCT) is applied to the control of a two degrees-of-freedom inverted pendulum mounted on an x-y table. Neural networks are used as auxiliary controllers for both the x axis and y axis of the PD controlled inverted pendulum. The DRCT method known to compensate for uncertainties at the trajectory level is used to control both the angle of a pendulum and the position of a cart simultaneously. Implementation of an on-line neural network learning algorithm has been implemented on the DSP board of the dSpace DSP system. Experimental studies have shown successful balancing of a pendulum on an x-y plane and good position control under external disturbances as well.

• Proceedings of the KIEE Conference
• /
• 2006.10d
• /
• pp.50-52
• /
• 2006

This paper Proposes a new method of on-line estimation for rotor resistance of the induction motor in the indirect vector controlled drive, using artificial neural network (ANN). The back propagation algorithm is used for training of the neural networks. The error between the desired state variable of an induction motor and actual state variable of a neural network model is back propagated to adjust the weight of a neural network model, so that the actual state variable tracks the desired value. The performance of rotor resistance estimator and torque and flux responses of drive, together with these estimators, are investigated variations rotor resistance from their nominal values. The rotor resistance are estimated analytically, using the proposed ANN in a vector controlled induction motor drive.