• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.113-118
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• 1999

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important n the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.286-286
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.154-161
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of Biomedical Engineering Research
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• v.19 no.1
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• pp.39-46
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• 1998

In this paper, we present a neural network identification and a control of highly complicated nonlinear left ventricular assist device(LVAD) system with a pneumatically driven mock circulation system. Generally, the LVAD system needs to compensate for nonlinearities. It is necessary to apply high performance control techniques. Fortunately, the neural network can be applied to control of a nonlinear dynamic system by learning capability. In this study, we identify the LVAD system with neural network identification(NNI). Once the NNI has learned the dynamic model of the LVAD system, the other network, called neural network controller(NNC), is designed for a control of the LVAD system. The ability and effectiveness of identifying and controlling the LVAD system using the proposed algorithm will be demonstrated by computer simulation.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.274-281
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• 1998

This paper describes a visual control of autonomous vehicle using neural network. Visual control for road-following of autonomous vehicle is based on road image from camera. Road points on image are inputs of controller and vehicle speed and steering angle are outputs of controller using neural network. Simulation study confirmed the visual control of road-following using neural network. For experimental test, autonomous electric vehicle is designed and driving test is realized

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.255-258
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• 2003

It is hard to centrol the wheeled mobile robot because of uncertainty of modeling, non-holonomic constraint and so on. To solve the problems, we design the controller of wheeled mobile robot based on fuzzy-neural network algorithm. In this paper, we should research the problem of classical controller for path-tracking algorithm and design of Fuzzy-Neural Network algorithm controller. Classical controller acquired different control value according to change of initial position and direction. In this control value having very difficult and having acquired a lot of trial and error Fuzzy is implemented to adaptive adjust control value by error and change of error and neural network is implemented to adaptive adjust the control gain during the optimization. The computer simulation shows that the proposed fuzzy-neural network controller is effective.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.3
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• pp.487-492
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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 is composed of neural network of state space model type, performs for a unknown nonlinear 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.

• Structural Engineering and Mechanics
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• v.26 no.4
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• pp.377-392
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• 2007

A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.201-204
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• 1998

This paper presents a new construction method of candidate controllers using Multi-modal Neural Network(MNN). To improve a control performance of multiple controller, we construct, candidate controllers which consist of MNN. MNN can learn more complicated function than multilayer neural network. MNN consists of preprocessing module and neural network module. The preprocessing module transforms input signals into spectra which are used as input of the following neural network module. We apply the proposed method to multiple control system which controls the cart-pole balancing system and show the effectiveness of the proposed method.