• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.729-737
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• 1998

In this paper, using $\mu$ synthesis algorithm with structured uncertainty, we design controller and apply it for the Two-Inertia resonance(TMS: Two Mass Spring) system. The TMS system is one of the simplest models which generate a torsional vibration. In this system, it is required to design a controller achieving the control performance while suppressing the torsional vibration. Furthermore, when vibration frequency for the system is varying by reason of parameter variations, we should consider parameter variations in controller design. Then, we design two other controller schemes of the PI controller and the standard $H_{\infty}$ controller and compare these controllers with the controller designed by the $\mu$ synthesis robust control method by using simulations and experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.426-426
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• 2000

When real robot manipulators arc mathematically modeled, uncertainties are not avoidable. The uncertainties are often nonlinear and time varying, The uncertain factors come from imperfect knowledge of system parameters, payload change, friction, external disturbance and etc. We proposed a class of robust hybrid position/force control of manipulators and provided the stability analysis in the previous work. In the work, we propose a class of adaptive robust hybrid position/force control of manipulators with bound estimation and the stability based on Lyapunov function is presented. Especially, this controller does not need the information of uncertainty bound. The simulation results are provided to show the effectiveness of the algorithm.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.667-676
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• 2000

This paper focuses on the problem of detecting multiple leverage points and outliers in multivariate linear models. It is well known that he identification of these points is affected by masking and swamping effects. To identify them, Rousseeuw(1985) used robust estimators of MVE(Minimum Volume Ellipsoids), which have the breakdown point of 50% approximately. And Rousseeuw and van Zomeren(1990) suggested the robust distance based on MVE, however, of which the computation is extremely difficult when the number of observations n is large. In this study, e propose a new algorithm to reduce the computational difficulty of MVE. The proposed method is powerful in identifying multiple leverage points and outlies and also effective in reducing the computational difficulty of MVE.

• Transactions of the Society of Information Storage Systems
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• v.1 no.2
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• pp.137-142
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• 2005

In this paper, we propose a new servo system design strategy to reduce the position error signal(PES) and track mis-registration(TMR) in magnetic disk drive systems. The proposed method provides a systematic design procedure based on the plant model and an optimal solution via an optimization with a 'Robust Random Neighborhood Search(RRNS)' algorithm. In addition, it guarantees the minimum PES level as well as stability to parametric uncertainties. Furthermore, the proposed method can be used to estimate the performance at the design stage and thus can reduce the cost and time for the design of the next generation product. The reduction of PES as well as robust stability is demonstrated by simulation and experiments.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.651-662
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• 2017

We propose an alternative procedure to select penalty parameter in $L_1$ penalized robust regression. This procedure is based on marginalization of prior distribution over the penalty parameter. Thus, resulting objective function does not include the penalty parameter due to marginalizing it out. In addition, its estimating algorithm automatically chooses a penalty parameter using the previous estimate of regression coefficients. The proposed approach bypasses cross validation as well as saves computing time. Variable-wise penalization also performs best in prediction and variable selection perspectives. Numerical studies using simulation data demonstrate the performance of our proposals. The proposed methods are applied to Boston housing data. Through simulation study and real data application we demonstrate that our proposals are competitive to or much better than cross-validation in prediction, variable selection, and computing time perspectives.

• 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.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.929-930
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• 2006

In this paper, we propose a planning algorithm which automatically generates robust behavior plans for service robots in the dynamically changing environments. The proposed method searches for paths to perform the given tasks in the physical space and the configuration space where tasks are described. And then, the characteristics of paths for successfully performed task are abstracted and generalized to build an ordered-tree structure. The resulting robust behavior plans guarantee that the given tasks are successfully performed. The validity of our method is tested by simulation work for a pushing-box task.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.289-298
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• 1999

We consider a minimax approach to make a design robust to many types or uncertainty arising in reality when dealing with non-normal linear models. We try to build a design to protect against the worst case, i.e. to improve the "efficiency" of the worst situation that can happen. In this paper, we especially deal with the generalized linear model. It is a known fact that the generalized linear model is a universal approach, an extension of the normal linear regression model to cover other distributions. Therefore, the optimal design for the generalized linear model has very similar properties as the normal linear model except that it has some special characteristics. Uncertainties regarding the unknown parameters, link function, and the model structure are discussed. We show that the suggested approach is proven to be highly efficient and useful in practice. In the meantime, a computer algorithm is discussed and a conclusion follows.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.2
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• pp.80-88
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• 2005

In this paper, an robust adaptive backstepping controller is proposed for the speed control of separately excited DC motor with uncertainties and disturbances. Armature and field resistance, damping coefficient and load torque are considered as uncertainties and noise generated at applying load torque to motor is also considered. It shows that the backstepping algorithm can be used to solve the problems of nonlinear system very well and robust controller can be designed without the variation of adaptive law. Simulation and experiment results are provided to demonstrate the effectiveness of the proposed controller.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2602-2604
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• 2000

In this paper, we study robust linear optimal model following servo system in the presence of disturbances and parameter perturbations. A technique to directly design the generalized differential operator based unified control system that covers both differential operator based continuous time and delta operator based discrete time case is presented. The quadratic criterion function for a linear system is used to design the robust unified servo control system. This servo control system is designed by applying a simple genetic algorithm to follow the output of the reference model optimally. The characteristics of the proposed servo system are analysed and simulated to verify the robustness.