• Journal of Advanced Marine Engineering and Technology
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• v.23 no.4
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• pp.480-487
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• 1999

In this paper we propose a neural network precompensated PID(NNP PID) controller for load frequency control of 2-area power system. While proportional integral derivative(PID) controllers are used in power system they have many problems because of high nonlinearities of the power system So a neural network-based precompensation scheme is adopted into a conventional PID controller to obtain a robust control to the nonlinearities. The applied neural network precompen-sator uses an error back-propagation learning algorithm having error and change of error as inputand considers the changing component of forward term of weighting factor for reducing of learning time. Simulation results show that the proposed control technique is superior to a conventional PID controller and an optimal controller in dynamic responses about load disturbances. The pro-posed technique can be easily implemented by adding a neural network precompensator to an existing PID controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1821-1826
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• 2009

An adaptive neural controller for perturbed strict-feedback nonlinear system is proposed. All the previous adaptive neural (or fuzzy) controllers are based on the backstepping scheme where the universal approximators are employed in every design steps. These schemes involve virtual controls and their time derivatives that make the stability analysis and implementation of the controller very complex. This fact is called 'explosion of complexty ' since the complexity grows exponentially as the system dynamic order increases. The proposed adaptive neural control scheme adopt the backstepping design procedure only for determining ideal control law and employ only one neural network to approximate the finally selected ideal controller, which makes the controller design procedure and stability analysis considerably simple compared to the previously proposed controllers. It is shown that all the time-varing signals containing tracking error are stable in the Lyapunov viewpoint.

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

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.355-355
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• 2000

The position control accuracy of a robot manipulator is significantly deteriorated when a long arm robot is operated at a high speed. This paper presents a very simple sliding mode control which eliminates multiple mode residual vibration in a 개bot manipulator. The neural network is used to avoid that sliding mode condition is deviated due to the change of system parameter and disturbance. This paper is suggested control system which designed by sliding mode controller using neural network. The effectiveness of proposed scheme is demonstrated through computer simulation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.159-167
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• 2000

During the operation of crane system in the container yard, it is necessary to control the crane trolley position so that the swing of the hanging container is minimized. Recently an automatic control system with high speed and rapid transportation is required. Therefore, we designed a controller to control the crane system with disturbances and weight change. In this paper, we present the neural network two degree of freedom PID controller to control the swing motion and trolley position. Then we executed the computer simulation to verify the performance of the proposed controller and compared the performance of the neural network PID controller with our proposed controller in terms of the rope swing and the precision of position control. Computer simulation results show that the proposed controller has better performances than neural network PID with disturbances.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2254-2259
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• 2005

In this paper, we propose a wavelet based fuzzy neural network(WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges advantages of neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of mobile robot using the gradient descent(GD) method. In addition, an approach that uses adaptive learning rates for the training of WFNN controller is driven via a Lyapunov stability analysis to guarantee the fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control performance of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.56-62
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• 2001

This paper presents a neural network based variable structure control scheme for nonlinear systems. In this scheme, a set of local variable structure control laws are designed on the basis of the linear models about preselected representative points which cover the range of the system operation of interest. From the combination of the set of local variable structure control laws, neural networks infer the approximate control input in between the operating points. The neural network based variable structure control alleviates the effects of model uncertainties, which cannot be compensated by the control techniques using feedback linearization. It also relaxes the discontinuity in the system’s behavior that appears when the control schemes based on the family of the linear models are applied to nonlinear systems. Simulation results of a ball and beam system, to which feedback linearization cannot be applied, demonstrate the feasibility of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.703-707
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• 1994

This paper proposes a method of learning control in DC servomotor using a neural network. First we estimate the pulse transfer function of the servo system with an unknown load, then we determine the best gains of I-PD control system using a neural network. Each time the load changes, its best gains of the I-PD control system is computed by the neural network. And the best gains and its pulse transfer function for the case are stored in the memory. According the increase of the set of gains and its pulse transfer function, the learning control system can afford the most suitable I-PD gains instantly.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.135-135
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• 2000

In this Paper. the DSP implementation of induction motor drive is Presented on the viewpoint of the design and experiment. The speed estimation of control system for induction motor drive is designed on the base of neural network speed estimator. This neural network speed estimator is experimentally applied to the induction motor system. This system Provides the satisfactory results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.494-497
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• 2002

In this paper, we proposed a control method of an unknown nonlinear system using a dynamical neural network. The proposed method performs for a nonlinear system with unknown system, identification with using the dynamical neural network, and then a nonlinear adaptive controller is designed with these identified informations. In order to verify the effectiveness of the proposed method, we simulated one-link manipulator. The simulation results showed the effectiveness of using the dynamical neural network in the adaptive control of one-link manipulator.