• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1843-1846
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• 2004

This paper considers a robust observer-based $H_{\infty}$ controller design method for descriptor systems with parameter uncertainties using just one LMI condition. The sufficient condition for the existence of controller and the controller design method are presented by a perfect LMI condition in terms of all variables using singular value decomposition, Schur complement, and change of variables. Therefore, one of the main advantages is that a robust observer-based $H_{\infty}$ controller is found by solving one LMI condition compared with existing results. Numerical example is given to illustrate the effectiveness of the proposed controller design method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.1
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• pp.11-16
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• 2005

This paper considers a robust observer-based H/sub ∞/ controller design method for singular systems with parameter uncertainties using an LMI condition. The sufficient condition for the existence of controller and the controller design method are presented by a perfect LMI condition in terms of all variables using singular value decomposition, Schur complement, and change of variables. Therefore, one of the main advantages is that a robust observer-based H/sub ∞/ controller can be established by solving one LMI condition compared with existing results. Numerical example is given to illustrate the effectiveness of the proposed controller design method.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1832-1833
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• 2006

This paper presents a design method of the wavelet neural network(WNN) controller based on a direct adaptive control scheme for the intelligent control of Autonomous Underwater Vehicle(AUV) steering systems. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome nonlinearities and uncertainty. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and original signal of AUV model that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by gradient-descent method. Through computer simulations, we demonstrate the effectiveness of the proposed control method.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.812-817
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• 2009

In this paper, a new method to design sliding surfaces using eigenstructure assignment is proposed. Most conventional methods for constructing the surfaces require special form like canonical or regular canonical form of system matrices. But the proposed method can be applied to arbitrary system matrices. Futhermore, the surface matrix, C can be decided for the matrix multiplication, CB to have a designated form. SVD is used to decide desirable eigenvectors explicitly. To verify the proposed algorithm, a sliding mode controller for a multivariable system with matched uncertainty is constructed. The controller is designed to guarantee minimum approach velocity to the sliding surface.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.381-385
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• 1992

In this paper, dynamic modeling and adaptive control problems for a space robot system are discussed. The space robot consist of a robot manipulator mounted on a free-floating base where no attitude control is applied. Using an extended robot model, the entire space robot can be viewed as an under-actuated robot system. Based on nonlinear control theory, the extended space robot model can then be decomposed into two subsystems: one is input-output exactly linearizable, and the other is unlinearizable and represents an internal dynamics. With this decomposition, a normal form-augmentation approach and an augmented state-feedback control are proposed to facilitate the design of adaptive control for the space robot system against parameter uncertainty, unknown dynamics and unmodeled payload in space applications. We demonstrate that under certain conditions, the entire space robot can be represented as a full-actuated robot system to avoid the inclusion of internal dynamics. Based on the dynamic model, we propose an adaptive control scheme using Cartesian space representation and demonstrate its validity and design procedure by a simulation study.

• Progress in Superconductivity and Cryogenics
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• v.15 no.1
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• pp.19-24
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• 2013

In this paper, the Superconductor flywheel energy storage system (SFESS) was used for the load frequency control (LFC) of an interconnected 2 area power system. The robust SFESS controller using quantitative feedback theory (QFT) was designed to improve control performance in spite of parameter uncertainty and unexpected disturbances. An overlapping decomposition method was applied to simplify SFESS controller design for the interconnected 2 area power system. The model for simulation of the interconnected 2 area power system included the reheat steam turbine, governor, boiler dynamics and nonlinearity such as governor deadband and generation rate constraint (GRC). To verify robust performance of proposed SFESS controller, dynamic simulation was performed under various disturbances and parameters variation of power system. The results showed that the proposed SFESS controller was more robust than the conventional method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.465-470
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• 2007

In order to investigate the stochastic behavior of Mindlin plate under imperfection in the material and geometrical parameters, a stochastic finite element formulation is proposed. The effects of inter-correlations between random parameters on the response variability are also observed. The contribution from the random Poisson ratio is taken into account adopting a stochastic decomposition scheme. which expands the constitutive matrix into an infinite series of sub-matrices. In order to demonstrate the adequacy of the proposed scheme, a square plate with simple and fixed support is taken as an example, and the results are compared with those given in previous research in the literature as well as with the results of Monte Carlo analysis.

• Structural Engineering and Mechanics
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• v.4 no.6
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• pp.703-715
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• 1996

In stochastic analysis, the randomness of the structural parameters is taken into consideration and the response variability is obtained in addition to the conventional (mean) response. In the present paper the structural response variability of plate structure is calculated using the weighted integral method and is compared with the results obtained by different methods. The stochastic field is assumed to be normally distributed and to have the homogeneity. The decomposition of strain-displacement matrix enabled us to extend the formulation to the stochastic analysis with the quadratic elements in the weighted integral method. A new auto-correlation function is derived considering the uncertainty of plate thickness. The results obtained in the numerical examples by two different methods, i.e., weighted integral method and Monte Carlo simulation, are in a close agreement. In the case of the variable plate thickness, the obtained results are in good agreement with those of Lawrence and Monte Carlo simulation.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.216-223
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• 2015

The autonomous underwater vehicle (AUV) is being widely researched in order to achieve superior performance when working in hazardous environments. This research focuses on using image processing techniques to estimate the AUV's egomotion and the changes in orientation, based on image frames from different time frames captured from a single high-definition web camera attached to the bottom of the AUV. A visual odometry application is integrated with other sensors. An internal measurement unit (IMU) sensor is used to determine a correct set of answers corresponding to a homography motion equation. A pressure sensor is used to resolve image scale ambiguity. Uncertainty estimation is computed to correct drift that occurs in the system by using a Jacobian method, singular value decomposition, and backward and forward error propagation.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2414-2416
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• 2003

In this raper, we present a Wavelet Neural Network(WNN) approach to the solution of the tracking problem for mobile robots that possess complexity, nonlinearity and uncertainty. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome the problems caused by local minima of optimization and various uncertainties. This network structure is helpful to determine the number of the hidden nodes and the initial value of weights with compact structure. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and the pose of a mobile robot that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by the gradient-descent method. Through computer simulations, we demonstrate the effectiveness and feasibility of the proposed control method.