• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.236-242
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• 1992

Multi-layered neural networks are applied to the identification and control of nonlinear dynamical system. Traditional adaptive control techniques can only deal with linear systems or some special nonlinear systems. A scheme for combining multi-layered neural networks with model reference network techniques has the capability to learn the nonlinearity and shows the great potential for adaptive control. In many interesting cases the system can be described by a nonlinear model in which the control input appears linearly. In this paper the identification of linear and nonlinear part are performed simultaneously. The projection algorithm and the new estimation method which uses the delta rule of neural network are compared throughout the simulation. The simulation results show that the identification and adaptive control schemes suggested are practically feasible and effective.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.95-100
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• 2001

In this paper, we proposed the robust control scheme for a class of nonlinear dynamical systems using output feedback linearization method. The presented control scheme is based on the VSS. We assume that the nonlinear dynamical system is minimum phase, the relative degree of the system is r＜n and zero dynamics is stable. It is also shown that the global asymtotically stability is guaranted. And we verified that the proposed control scheme Is the feasible through a computer simulation.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.177-182
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• 2003

The robust output tracking control problem of general nonlinear MIMO systems is discussed. The robustness against parameter uncertainties is considered. In this paper, we proposed the robust output tracking control scheme for a class of MIMO nonlinear dynamical systems using output feedback linearization method. The presented control scheme is based on the VSS. We assume that the nonlinear dynamical system is minimum phase, the relative degree of the system is r$_{1}$＋r$_{2}$＋…r$_{m}$$\leq$ n and zero dynamics is stable. It is shown that the outputs of the closed-loop system asymptotically track given output trajectories despite the uncertainties while maintaining the boundedness of all signals inside the loop. And we verified that the proposed control scheme is then applied to the control of a two degree of freedom (DOF) robotic manipulator with payload.d.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.388-388
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• 2000

The complexity of nonlinear systems makes it difficult to ascertain their behavior using classical methods of analysis. Many efforts have been focused on the advanced algorithms and techniques that hold the promise of improving real-time optimal control while at the same time providing higher accuracy. In this paper, a fuzzy cell mapping method of real-time optimal control far nonlinear dynamical systems is proposed. This approach combines fuzzy logic with cell mapping techniques in order to find the optimal input level and optimal time interval in the finite set which change the state of a system to achieve a desired obiective. In order to illustrate this method, we analyze the behavior of an inverted pendulum using fuzzy cell mapping.

• The Pure and Applied Mathematics
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• v.16 no.4
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• pp.427-441
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• 2009

The method of Lattice Dynamical System is used to establish a global model on an infinite chain of many local markets interacting each other through a diffusion of prices between them. This global model extends the Walrasian evolutionary cobweb model in an independent single local market to the global market evolution. We assume that each local market has linear decreasing demands and quadratic supplies with naive predictors, and investigate the stationary behaviors of global price dynamics and show that their dynamics are conjugate to those of $H{\acute{e}}non$ maps and hence can exhibit complicated behaviors such as period-doubling bifurcations, chaos, and homoclic orbits etc.

• Nuclear Engineering and Technology
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• v.27 no.6
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• pp.845-852
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• 1995

We investigate the concurrent solution of differential equations by the waveform relaxation (WR) method, an iterative method for analyzing linear and nonlinear dynamical systems in the time do-main. The method, at each iteration, decomposes the dynamical system into several subsystems, each of which is analyzed for the entire given time interval. The method, when efficiently implemented, results in algorithms with a highly parallelizable concurrent fraction. In this paper, the waveform relaxation method is introduced and applied to two types of reactor dynamics problems. It is concluded that the U method can be applied to reactor dynamics equations, but that its parallel performance on the KMRR dynamics is only modest.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.755-759
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• 1989

This paper considers an active vibration control system based on pole placement incorporating the internal model principle when the system is subjected to disturbances which are generated by a linear dynamical system. Experimental results are presented which show the effectiveness of the method when the system is excited by a sine wave disturbance and system parameters are knwon. An adaptive control design is also discussed.

• Journal of Ocean Engineering and Technology
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• v.22 no.4
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• pp.97-105
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• 2008

This paper represents the experimental identification of a finite-dimensional dynamical plant model for the HEMIRE Remotely Operated Vehicle. The experiments were conducted during sea trials in the East Sea in October 2006 and peer testing by the South Sea Research Institute in January 2007. A least-squares method was employed to identify decoupled single degree-of-freedom plant dynamical models for the X, Y, Z and heading degree-of-freedom from experimental data. The performance of the identified plant dynamical model was evaluated by directly comparing simulations of the identified plant model to the experimentally observed motion data from the actual vehicle.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.285-288
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• 1998

A least-squares identification method is studied that estimates a finite number of coefficients in the series expansion of a transfer function, where the expansion is in terms of recently introduced generalized basis functions, We will expand and generalize the orthogonal functions as basis functions for dynamical system representations. To this end, use is made of balanced realizations as inner transfer functions. The orthogonal functions can be considered as generalizations of, for example, the pulse functions, Laguerre functions, and Kautz functions, and give rise to an alternative series expansion of rational transfer functions. We show that the Laplace transform of the expansion for some sets$\Psi_{\kappa}(Z)$ is equivalent to a series expansion . Techniques based on this result are presented for obtaining the coefficients $c_{n}$ as those of a series. One of their important properties is that, if chosen properly, they can substantially increase the speed of convergence of the series expansion. This leads to accurate approximate models with only a few coefficients to be estimated. The set of Kautz functions is discussed in detail and, using the power-series equivalence, the truncation error is obtained.

• Journal of the Chungcheong Mathematical Society
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• v.15 no.1
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• pp.61-73
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• 2002

We consider the inverse shadowing property of a dynamical system which is an "inverse" form of the shadowing property of the system. In particular, we show that every Morse-Smale system f on a compact smooth manifold has the inverse shadowing property with respect to the class $\mathcal{T}_h(f)$ of continuous methods generated by homeomorphisms, but the system f does not have the inverse\mathrm{T} shadowing property with respect to the class $\mathcal{T}_c(f)$ of continuous methods.