• Journal of Ocean Engineering and Technology
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• v.16 no.1
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• pp.8-15
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• 2002

This paper presents a neural net based nonlinear adaptive controller for an autonomous underwater vehicle (AUV). AUV's dynamics are highly nonlinear and their hydrodynamic coefficients vary with different operational conditions, so it is necessary for the high performance control system of an AUV to have the capacities of learning and adapting to the change of the AUV's dynamics. In this paper a linearly parameterized neural network is used to approximate the uncertainties of the AUV's dynamic, and the basis function vector of network is constructed according to th AUV's physical properties. A sliding mode control scheme is introduced to attenuate the effect of the neural network's reconstruction errors and the disturbances in AUV's dynamics. Using Lyapunov theory, the stability of the presented control system is guaranteed as well as the uniformly boundedness of tracking errors and neural network's weights estimation errors. Finally, numerical simulations for motion control of an AUV are performed to illustrate the effectiveness of the proposed techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3133-3151
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• 2012

The optimal price strategy selection of two bounded rational cognitive mobile virtual network operators (MVNOs) in a duopoly spectrum sharing market is investigated. The bounded rational operators dynamically compete to sell the leased spectrum to secondary users in order to maximize their profits. Meanwhile, the secondary users' heterogeneous preferences to rate and price are taken into consideration. The evolutionary game theory (EGT) is employed to model the dynamic price strategy selection of the MVNOs taking into account the response of the secondary users. The behavior dynamics and the evolutionary stable strategy (ESS) of the operators are derived via replicated dynamics. Furthermore, a reward and punishment mechanism is developed to optimize the performance of the operators. Numerical results show that the proposed evolutionary algorithm is convergent to the ESS, and the incentive mechanism increases the profits of the operators. It may provide some insight about the optimal price strategy selection for MVNOs in the next generation cognitive wireless networks.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.362-362
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• 2000

The purpose of this thesis is to reduce force ripple of linear pulse motor(LPM) using neural network and to enhance precision. In order to this, we propose a new controller using a neural network to compensate disturbances. The structure includes adaptation block which learns the dynamics of the periodic disturbance and forces the interferences, caused by disturbances. The proposed controller compensates an unmodeled dynamics in the LPM. The neural network changes a current command to reduce position error and force ripple of the LPM. We compare proposed controller with PI controller. Simulation result shows that the proposed controller has better performance than a PI controller without neural network.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2004.04a
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• pp.86-96
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• 2004

Identification of dynamics of short circulation during grade change operations in paper mills is very important for the effective plant operation. In the present study a prediction method of One Pass Retention(OPR) is proposed based on the neural network. The present method is used to analyze the dynamics of short circulation during grade change. Properties of the product paper largely depend upon the change in the OPR. In the present study the OPR is predicted from the training of the network by using grade change operation data. The results of the prediction are applied to the modeling equation to give flow rates and consistencies of short circulation.

• Journal of Institute of Control, Robotics and Systems
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• v.1 no.2
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• pp.88-93
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• 1995

This paper presents a study using neural networks in the design of the tracking controller of robotic systems. Our strategy is to put to use the available knowledge about the robot manipulator, such as estimation models, in the contoller design via the computed torque method, and then to add the neural network to control the remaining uncertainty. The neural network used here learns to provide the inverse dynamics of the plant uncertainty, and acts as an inverse controller. In the simulation study, we verify that the proposed neural network controller is robust not only to structured uncertainties, but also to unstructured uncertainties such as friction models.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.6
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• pp.142-155
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• 2000

A MIMO model reference control scheme incorporating the variable structure theory for a vehicle four wheel steering system(4WS) is proposed and evaluated for a class of continuous-time nonlinear dynamics with known or unknown uncertainties. The scheme employs an neural network to identify the plant systems, where the neural network estimates the nonlinear dynamics of the plant. By the Lyapunov direct method, the algorithm is proven to be globally stable, with tracking errors converging to the neighborhood of zero. The merits of this scheme is that the global system stability is guaranteed and it is not necessary to know the exact structure of the system. With the resulting identification model which contains the neural networks, it does not need higher degrees of freedom vehicle model than 3 degree of freedom model. Th proposed scheme is applied to the active four wheel system and shows the validity is used to investigate vehicle handing performances. In simulation of the J-turn maneuver, the reduction of yaw rate overshoot of a typical mid-size car improved by 30% compared to a two wheel steering system(2WS) case, resulting that the proposed scheme gives faster yaw rate response and smaller side angle than the 2WS case.

• Journal of Communications and Networks
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• v.8 no.2
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• pp.241-252
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• 2006

Traditional service-level agreements (SLAs), defined by average delay or packet loss, often camouflage the instantaneous performance perceived by end-users. We define a set of metrics for service availability to quantify the performance of Internet protocol (IP) backbone networks and capture the impact of routing dynamics on packet forwarding. Given a network topology and its link weights, we propose a novel technique to compute the associated service availability by taking into account transient routing dynamics and operational conditions, such as border gateway protocol (BGP) table size and traffic distributions. Even though there are numerous models for characterizing topologies, none of them provide insights on the expected performance perceived by end customers. Our simulations show that the amount of service disruption experienced by similar networks (i.e., with similar intrinsic properties such as average out-degree or network diameter) could be significantly different, making it imperative to use new metrics for characterizing networks. In the second part of the paper, we derive goodness factors based on service availability viewed from three perspectives: Ingress node (from one node to many destinations), link (traffic traversing a link), and network-wide (across all source-destination pairs). We show how goodness factors can be used in various applications and describe our numerical results.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.4
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• pp.414-420
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• 1997

An intelligent speed control system for marine diesel engines is presented. The approach adopt¬ed is to use a conventional PID controller for normal operation and a feedforward controller for adaptive control. The feedforward controller is a neural network. The neural network is the inverse dynamics model of the plant, which is being trained on line. The parametric model of the diesel engine is represented in a linear second-order system, with a first-order combustion part and a revolution part each at a normal operating point. The time delay in the control of the com¬bustion part is approximated to the first-order system. The tuned PID parameters are set based on the model for normal operating point. To obtain the inverse dynamics of the diesel engine system, two neural networks are used, one for inverse, the other for forward dynamics. The former is posi¬tioned across the plant to learn its inverse dynamics during operation, and the latter is placed in series with the controlled plant. Simulation results are presented to illustrate the applicability of the proposed scheme to intelligent adaptive control of diesel engines.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.12-18
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• 2010

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. Genetic algorithms are employed to optimize the fuzzy rules of FBFN. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.173-179
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• 2003

In this paper, a real-time multibody vehicle dynamics and control model has been developed for a virtual reality intelligent vehicle simulator. The simulator consists of low PCs for a virtual reality visualization system, vehicle dynamics and control analysis system a control loading system, and a network monitoring system. Virtual environment is created by 3D Studio Max graphic tool and OpenGVS real-time rendering library. A real-time vehicle dynamics and control model consists of a control module based on the sliding mode control for adaptive cruise control and a real-time multibody vehicle dynamics module based on the subsystem synthesis method. To verify the real-time capability of the model, cut-in, cut-out simulations have been carried out.