• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1516-1520
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• 2005

We proposed the indirect adaptive fuzzy model based sliding mode controller to control a nonaffine nonlinear systems. Takagi-Sugano fuzzy system is used to represent the nonaffine nonlinear system and then inverted to design the controller at each sampling time. Also sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. The proposed controller and adaptive laws guarantee that the closed-loop system is stable in the sense of Lyapunov and the output tracks a desired trajectory asymptotically.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.97-104
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• 2020

In this paper, we propose a method to derive the kernel function directly from the measured apparent earth resistivity. At this time, the kernel function is obtained through the process of solving a nonlinear system. Nonlinear systems with many variables are difficult to solve. This paper also introduces a method for converting nonlinear derived systems to linear systems. The kernel function is a function of the depth and resistance of the Earth's layer. Being able to derive an accurate kernel function means that we can estimate the earth parameters i.e. layer depth and resistivity. We also use various Earth models as simulation examples to validate the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.85-90
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• 2003

This paper presents an adaptive PID control scheme for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model is used to estimate the error of control input, and the parameters of PID controller are adapted using the error. The parameters of TSK fuzzy model also adapted to plant. The proposed algorithm allows designing adaptive PID controller which Is adapted to the uncertainty of nonlinear plant and the change of parameters. The usefulness of the proposed algorithm is also certificated by the several simulations.

• 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 Electronics Engineers of Korea SC
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• v.45 no.5
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• pp.8-12
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• 2008

In this paper, the robust state observer for nonlinear systems with unknown disturbance is proposed. The proposed method has an advantage in that it can reduce the effect of disturbance on estimation error of observer up to a specified level. Therefore, our design a roach can deal with a larger class of uncertain nonlinear system than the existing methods. The sufficient conditions on the existence of robust observer are characterized by well grown linear matrix inequality. Finally, an illustrative example is given to verify the proposed design scheme.

• Journal of Mechanical Science and Technology
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• v.19 no.spc1
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• pp.227-236
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• 2005

Multibody system dynamics is based on classical mechanics and its engineering applications originating from mechanisms, gyroscopes, satellites and robots to biomechanics. Multibody system dynamics is characterized by algorithms or formalisms, respectively, ready for computer implementation. As a result simulation and animation are most convenient. Recent developments in multibody dynamics are identified as elastic or flexible systems, respectively, contact and impact problems, and actively controlled systems. Based on the history and recent activities in multibody dynamics, recursive algorithms are introduced and methods for dynamical analysis are presented. Linear and nonlinear engineering systems are analyzed by matrix methods, nonlinear dynamics approaches and simulation techniques. Applications are shown from low frequency vehicles dynamics including comfort and safety requirements to high frequency structural vibrations generating noise and sound, and from controlled limit cycles of mechanisms to periodic nonlinear oscillations of biped walkers. The fields of application are steadily increasing, in particular as multibody dynamics is considered as the basis of mechatronics.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.124-133
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• 2004

Global climate variations are expected to affect local hydro-meteorological variables like precipitation and temperature. The Southern Oscillation (SO) is one of the major driving forces that give impact on regional and local climatic variation. The relationships between SO and local climate variation are, however, characterized by strong nonlinear variation patterns. In this paper, the nonlinear dynamic relationship between the Southern Oscillation Index (SOI), precipitation, and temperature in Fukuoka, Japan, is investigated using by a nonlinear multivariable approach. This approach is based on the joint variation of these variables in the phase space. The joint phase-space variation of SOI, precipitation, and temperature is studied with the primary objective to obtain a better understanding of the dynamical evolution of local hydro-meteorological variables affected by global atmospheric-oceanic phenomena.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1597-1600
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• 2010

In this paper, a direct controller for nonlinear plants using a neural network is presented. The composed of the control input by using RBF neural networks and auxiliary input to compensate for effects of the approximation errors and disturbances. In the results, using this scheme, the output tracking error between the plant and the reference model can asymptotically converge to zero in the presence of bounded disturbances and approximation errors. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.11
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• pp.505-513
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• 2001

This paper presents the new three-level optimal control scheme for the large scale nonlinear systems, which is based on fast walsh transform. It is well known that optimization for nonlinear systems leads to the resolution of a nonlinear two point boundary value problem which always requires a numerical iterative technique for their solution. However, Three-level costate coordination can avoid two point boundary condition in subsystem. But this method also has the defect that must solve high order differential equation in intermediate level. The proposed method makes use of fast walsh transform, therefore, is simple in computation because of solving algebra equation instead of differential equation.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.647-649
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• 1998

This paper describes a robust stabilization of single input nonlinear systems with parametric uncertainty. We first investigate differential flatness of the nominal nonlinear systems. If a single input system is differentially flat, it possesses a flat output. And we define coordinate transformation functions via successively differentiating the flat output, and we also consider the robust fictitious controls at every differentiation of the flat output. In the new coordinates the nonlinear system is transformed into the Brunovsky normal form with matched uncertainty. With a robust control based on the Lyapunov method, the robust stabilization is achieved.