• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.96-101
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• 2007

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an adaptive tracking control for robot manipulators using the radial basis function network (RBFN) that is e. kind of neural networks. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed adaptive tracking controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.70-75
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• 1997

This paper proposes a new approach to the design of fuzzy-neuro control for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.2
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• pp.99-104
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• 2002

In this paper, we construct a hybrid intelligent controller based on a fusion scheme of GA(Genetic Algorithm) and FCM(Fuzzy C-Means) clustering-based ANFIS(Adaptive Neuro-Fuzzy Inference System). In the structure identification, a set of fuzzy rules are generated for a given criterion by FCM clustering algorithm. In the parameter identification, premise parameters are optimally searched by adaptive GA. On the other hand, consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. Finally, we applied the proposed method to the truck backer-upper control and obtained a better performance than previous works.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.161-164
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• 2004

The yo-yo system has been introduced as an interesting plant to demonstrate the effectiveness of intelligent controllers. Having nonlinear and asymmetric characteristics, the yo-yo plant requires a controller quite different from conventional controllers such as PID. In this paper is presented an adaptive method of controlling the yo-yo system. Fuzzy logic controller based on human expertise is referred at first. Then, an adaptive fuzzy controller which has adaptation features against the variation of plant parameters is proposed. Finally, experimental results are presented.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.693-695
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• 1998

Fuzzy controller design consists of intuition, and any other information about how to control system, into a set of rules. These rules can then be applied to the system. It is very important to decide parameters of IF-THEN rules. Because fuzzy controller can make more adequate force to the plant by means of parameter optimization, which is accomplished by learning procedure. In this paper, we apply fuzzy controller designed to the inverted pendulum.

• Wind and Structures
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• v.22 no.6
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• pp.635-662
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• 2016

This paper deals with the comparison of different control strategies for the Induction generators in wind energy conversion system. Mainly, two types of induction machines, Self excited induction generator (SEIG) and doubly Fed Induction generators (DFIG) are studied. The different control strategies for SEIG and DFIG are compared. For SEIG, Electronic load Controller mechanism, Static Compensator based voltage regulator are studied. For DFIG the main control strategy namely vector control, direct torque control and direct power control are implemented. Apart from these control strategies for both SEIG and DFIG to improve the performance, the ANFIS based controller is introduced in both STATCOM and DTC methods. These control methods are simulated using MATLAB/SIMULINK and performances are analyzed and compared.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.1
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• pp.78-84
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• 1998

The fluid system such as, the quantity control of raw water, chemicals control in the purification, the waste water system as well as in the feed water or circulation system of the power plant and the ventilation system is controlled with the valve and moter pump. The system's performance and the energy saving of the fluid systems depend on control of method and delicacy. Until, PI controller use in these system but it cannot control delicately because of the coupling in the system loop. In this paper we configure a single flow system to the multi variable system and suggest the application of 2-DOF PID controller and the tuning methods by the neural network to the electrical power of the flow control system. the 2-DOF controller follows to a setpoint has a robustness against the disturbance in the results of simulation. Keywords Title, Intelligent control, Neuro control, Flow control, 2 - DOF control., 2 - DOF control.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4B
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• pp.683-691
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• 2000

In this paper, we proposed the cell multiplexer using Hopfield neural network and the bandwidth predictor using the backpropagation neural network in order to make an accurate call setup decision. The cell multiplexer controls heterogeneous traffic and the bandwidth predictor estimates minimum bandwidth which satisfies traffic's QoS and maximizes throughput in network. Also, a novel connection admission controller decides on connection setup using the predicted bandwidth from bandwidth predictor and available bandwidth in networks. And then, we proposed a fuzzy traffic policer, when traffic sources violate the contract, takes an appropriate action and aim proved traffic shaper, which controls burstness which is one of key characteristics in multimedia traffic. We simulated the proposed controller. Simulation results show that the proposed controller outperforms existing controller.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.227-230
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• 1993

Recently, the development of novel control methodology enables us to improve the performance of AC-machine drives by using pulse width modulation (PWM) technique. Usually, the dynamic characteristic of induction motor (IM) has been represented by the 5-th order nonlinear differential equation. This dynamics, however, can be reduced to 3-rd order dynamics by applying direct control of IM input current. This methodology concludes that it is much easier to control IM by means of the field-oriented methods employing the current controller. Therefore a precise current control is crucial to achieve a high control performance both in dynamic and steady state operations. This paper presents an adaptive fuzzy current controller with artificial neural network (ANN) for field-oriented controlled IM. This new control structure is able to adaptively minimize a current ripple while maintaining constant switching frequency. Especially the proposed controller employs neuro-computing philosophy as well as adaptive learning pattern recognizing principles with respect to variations of the system parameters. The proposed approach is applied to the IM drive system, and its performance is tested through various simulations. Simulation results show that the proposed system, compared among several known classical methods, has a superb performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.619-625
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• 2000

A many control techniques have been proposed in order to improve the control performance of the discrete-time domain control system. In the position control system, the output of a controller is generally used as the input of a plant but the undesired noise is included in the output of a controller. Therefore there is a need to used a precompensator for rejecting the undesired noise. In this paper, The expanded I-PD control system with a precompensator is constructed. The precompensator and I-PD controller are designed by a neural network and these coefficients are changed automatically to be a desired response of system when the response characteristic of system is changed under a condition.