• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.598-600
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• 1998

In this paper, a robust controller is proposed to achieve the accurate tracking for uncertain robot manipulators with hydraulic actuator dynamics. The parameter uncertainty can be quantified by the linear parameterization technique. A switching controller is proposed to guarantee the global asymptotic stability of the plant. In order to eliminate the chattering caused by the switching controller, a smoothing controller is proposed using the boundary layer technique around the sliding surface. It is shown that the smoothing controller guarantees the uniform ultimate boundedness of the tracking, error. The proposed controller shows good better tracking performance.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.455-457
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• 1998

This paper designs a noise controller for the small cavity using Coefficient Diagram Method(CDM). In the small cavity system, there exist nonlinear characteristics such as uncertain-time delay and parameter variation. In the controller design of nonlinear system with uncertainty need to the higher order controller or complexity computation. The coefficient diagram is convenient implementation of the control system design method, that is utilized as a vehicle to collectively express the important features of the system and an improved version Kessler's standard form and the Lipatov stability condition of a constitutes the theoretical basis. Simultaneously, it is provided a desired specification, such as the robustness, the stability, faster response, and lower order controller. A simulation of the system with the proposed controller shows sufficient noise cancelation in small cavity.

• 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.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.171-179
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• 2008

This paper addresses the synthesis of an iterative learning controller for a class of linear systems with norm-bounded parameter uncertainties. We take into account an iterative learning algorithm with current cycle feedback in order to achieve both robust convergence and robust stability. The synthesis problem of the developed iterative learning control (ILC) system is reformulated as the ${\gamma}$-suboptimal $H_{\infty}$ control problem via the linear fractional transformation (LFT). A sufficient convergence condition of the ILC system is presented in terms of linear matrix inequalities (LMIs). Furthermore, the ILC system with fast convergence rate is constructed using a convex optimization technique with LMI constraints. The simulation results demonstrate the effectiveness of the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.52-57
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• 2006

In this paper, we have proposed a new adaptive fuzzy control algorithm based on Takagi-Sugeno fuzzy model. The regulation problem for the uncertain SISO nonlinear system is solved by the proposed algorithm. Using the advanced stability theory, the stability of the state, the control gain and the parameter approximation error is proved. Unlike the existing feedback linearization based methods, the proposed algorithm can guarantee the global stability in the presence of the singularity in the inverse dynamics of the plant. The performance of the proposed algorithm is demonstrated through the problem of balancing and swing-up of an inverted pendulum on a cart.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.4
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• pp.161-167
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• 2012

Energy conversion systems such as power inverters and converters are basically significant in establishing photovoltaic power systems to enhance power effectiveness. This paper proposes a new converter control method by using the Lyapunov redesign approach. We construct the proposed control mechanism linearly composed of nominal control and auxiliary control laws. The former is generally designed through a well-known power electronic technology and the latter is implemented to compensate real-time control error due to uncertain natures of converter systems in practice. For realizing adaptive control capability in the proposed control mechanism, a control parameter vector is estimated by utilizing a steepest descent based optimization method. We carry out numerical simulation with Matlab(c) software to demonstrate reliability of the proposed converter control system and conduct a comparative study to prove its superiority by comparing with a generic converter control methodology.

• Structural Engineering and Mechanics
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• v.56 no.5
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• pp.767-785
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• 2015

The stochastic finite element method is employed to obtain a stochastic dynamic model of angled beams subjected to impact loads when uncertain material properties are described by random fields. Using the perturbation technique in conjunction with a precise time integration method, a random analysis approach is developed for efficient analysis of random elastic waves. Formulas for the mean, variance and covariance of displacement, strain and stress are introduced. Statistics of displacement and stress waves is analyzed and effects of bend angle and material stochasticity on wave propagation are studied. It is found that the elastic wave correlation in the angled section is the most significant. The mean, variance and covariance of the stress wave amplitude decrease with an increase in bend angle. The standard deviation of the beam material density plays an important role in longitudinal displacement wave covariance.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.330-336
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• 2005

An intelligent control method is proposed for control of rigid robot manipulators which achieves exponential tracking of repetitive robot trajectory under uncertain operating conditions such as parameter uncertainty and unknown deterministic disturbance. In the learning controller, exponentially stable learning algorithms are combined with stabilizing computed error feedforward and feedback inputs. It is shown that all the error signals in the learning system are bounded and the repetitive robot motion converges to the desired one exponentially fast with guaranteed convergence rate. An engineering workstation based control system is built to verify the effectiveness of the proposed control scheme.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1195-1198
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• 1993

We investigate a systematic design procedure of automated rule generation of fuzzy logic based controller for uncertain dynamic systems such as an engine dynamic model.“Automated Tuning”means autonomous clustering or collection of such meaningful transitional relations in the state-space. Optimal control strategies are included in the design procedures, such as minimum squared error, minimum time, minimum energy or combined performance criteria. Fuzzy feedback control systems designed by the cell-state transition method have the properties of closed-loop stability, robustness under parameter variabtions, and a certain degree of optimality. Most of all, the main advantage of the proposed approach is that reliability can be potentially increased even if a large grain of uncertainty is involved within the control system under consideration. A numerical example is shown in which we apply our strategic fuzzy controller design to a highly nonlinear model of engine idle speed contr l.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.11
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• pp.2103-2108
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• 2011

In this paper, the problem of passivity analysis for neural networks with time-varying delays and norm-bounded parameter uncertainties is considered. By constructing a new augmented Lyapunov functional, a new delay-dependent passivity criterion for the network is established in terms of LMIs (linear matrix inequalities) which can be easily solved by various convex optimization algorithms. Two numerical example are included to show the effectiveness of proposed criterion.