• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.275-280
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• 2005

The principal context of this research is the approach to an artificial neural network algorithm which solves multivariable nonlinear equation systems by estimating the state of line power flow. First a dynamical neural network with feedback is used to find the minimum value of the objective function at each iteration of the state estimator algorithm. In second step a two-layer neural network structures is derived to implement all of the different matrix-vector products that arise in neural network state estimator analysis. For hardware requirements, as they relate to the total number of internal connections, the architecture developed here preserves in its structure the pronounced sparsity of power networks for which state the estimator analysis is to be carried out. A principal feature of the architecture is that the computing time overheads in solution are independent of the dimensions or structure of the equation system. It is here where the ultrahigh-speed of massively parallel computing in neural networks can offer major practical benefit.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.1045-1050
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• 1992

A new sliding surface for a viaraible structure control(VSC) law is emplyed to achieve fast and robust path tracking in a class of second-order nonlinear unceratin dynamical systems. The surface onitialy passes arbitrarily given initial conditions and subsequently moves towards a predetermined surface via rotaiting or/and shifting. We call it as a moving sliding surface(MSS). The surface is then incorporated with the VSC law which is constructed by imposing the sliding conditions in a special way. We primarily enforce the conditions which assume that once the system state is on a sliding surface that it is driven towards the zero state. Using the VSC law associatied with the MSS, it is shown that the tracking behavoirs are remarkably improved in the sene of the fastness and the robustness.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.322-331
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• 2014

Chaotic dynamics is an active area of research in biology, physics, sociology, psychology, physiology, and engineering. This interest in chaos is also expanding to the social scientific fields such as politics, economics, and argument of prediction of societal events. In this paper, we propose a dynamic model for addiction of tobacco. A proposed dynamical model originates from the dynamics of tobacco use, recovery, and relapse. In order to make an addiction model of tobacco, we try to modify and rescale the existing tobacco and Lorenz models. Using these models, we can derive a new tobacco addiction model. Finally, we obtain periodic motion, quasi-periodic motion, quasi-chaotic motion, and chaotic motion from the addiction model of tobacco that we established. We say that periodic motion and quasi-periodic motion are related to the pre-addiction or recovery stage, respectively. Quasi-chaotic and chaotic motion are related to the addiction stage and relapse stage, respectively.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2693-2695
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• 2000

Generally, neural networks can be used efficiently for the identification and control of nonlinear dynamical system, then it always needs to learn in order that the output values is closed to desired values. But, if plant input to control is limited to certain bounded values, former learning rules has the another problem. This paper demonstrates algorithm to control the bounded-input plant using neural network controller.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2699-2701
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• 2000

This paper presents the Runge-Kutta neural networks(RKNN's) using the Runge-Kutta approximation method and the orthogonal function for control of unknown dynamical systems described by ordinary differencial equations in high accuracy. These subnetworks of RKNN's are based on orthogonal function. Computer simulations show the usefulness of the proposed scheme.

• Advances in robotics research
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• v.2 no.1
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• pp.99-112
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• 2018

The main contribution of this work is the design of a field programmable gate array (FPGA) based ARX-Laguerre proportional-integral observation (PIO) system for fault detection and identification (FDI) in a multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. An ARX-Laguerre method was used in this study to dynamic modeling the robot manipulator in the presence of uncertainty and disturbance. To address the challenges of robustness, fault detection, isolation, and estimation the proposed FPGA-based PI observer was applied to the ARX-Laguerre robot model. The effectiveness and accuracy of FPGA based ARX-Laguerre PIO was tested by first three degrees of the freedom PUMA robot manipulator, yielding 6.3%, 10.73%, and 4.23%, average performance improvement for three types of faults (e.g., actuator fault, sensor faults, and composite fault), respectively.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.209-214
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• 1998

In this paper, we are dealing with the problem of controlling unknown nonlinear dynamical system by using neural networks. A novel error self-recurrent(ESR) neural model is presented to perform black-box identification. Through the various outcome of the experiment, a new neural network is seen to be considerably faster than the BP algorithm and has advantages of being less affected by poor initial weights and learning rate. These characteristics make it flexible to design the controller in real-time based on neural networks model. In addition, we design an adaptive PID controller that Keyser suggested by using ESR neural networks, and present a method on the implementation of adaptive controller based on neural network for practical applications. We obtained good results in the case of robot manipulator experiment.

• Earthquakes and Structures
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• v.5 no.2
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• pp.225-237
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• 2013

Excessive vibrations induced by earthquake excitation and wind load are an obstacle in design and construction of tall and super tall buildings. An innovative vibration control structure system (Mega-Sub Controlled Structure System-MSCSS) was recently proposed to further improve humans comfort and their safeties during natural disasters. Preliminary investigations were performed using a two dimensional equivalent simplified model, composed by 3 mega-stories. In this paper, a more reasonable and realistic scaled model is design to investigate the dynamical characteristics and controlling performances of this structure when subjected to strong earthquake motion. The control parameters of the structure system, such as the modulated sub-structures disposition; the damping coefficient ratio (RC); the stiffness ratio (RD); the mass ratio of the mega-structure and sub-structure (RM) are investigated and their optimal values (matched values) are obtained. The MSCSS is also compared with the so-called Mega-Sub Structure (MSS) regarding their displacement and acceleration responses when subjected to the same load conditions. Through the nonlinear time history analysis, the effectiveness and the feasibility of the proposed mega-sub controlled structure system (MSCSS) is demonstrated in reducing the displacement and acceleration responses and also improving human comfort under earthquake loads.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.83-91
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• 2004

The purpose of this paper is to obtain an accurate nonlinear system model to test various control schemes for a motion control system that requires high speed, robustness and accuracy. An industrial sewing machine equipped with a Brushless DC motor is considered. It is modeled by a neural network that is configured as an output-error dynamical system. The identified model is essentially a one step ahead prediction structure in which past inputs and outputs are used to calculate the current output. Using the model, a 2 degree-of-freedom PID controller to compensate the effects of disturbance without degrading tracking performance has been de-signed. In this experiment, it is not preferable for safety reasons to tune the controller online on the actual machinery. Experimental results confirm that the model is a good approximation of sewing machine dynamics and that the proposed control methodology is effective.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.2
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• pp.495-508
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• 1995

The self-excited vibrations of airfoil is related to the classical flutter problems, and it has been studied as a system with linear stiffness and small damping. However, since the actual aircraft wing and the many mechanical elements of airfoil type have various design variables and parameters, some of these could have strong nonlinearities, and the nonlinearities could be unexpectedly strong as the parameters vary. This abrupt chaotic behavior undergoes ordered routes, and the behaviors after these routes are uncontrollable and unexpectable since it is extremely sensitive to initial conditions. In order to study the chaotic behavior of the system, three parameters are considered, i.e., free-stream velocity, elastic distance and zero-lift angle. If the chaotic parameter region can be identified from the mathematically modeled nonlinear differential equation system, the designs which avoid chaotic regions could be suggested. In this study, by using recently developed dynamically system methods, and chaotic regions on the parameter plane will be found and the safe design variables will be suggested.