• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.997_998
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• 2009

Nowadays DC-DC converter has been used widely in electronic production. It has a high requirement in wide input voltage, load variations, stability, providing a fast transient response and the most important thing is that it can be applied easily and efficiently. However, it is not easy to be controlled because of nonlinear system. This study introduces a fuzzy linear control design method for nonlinear systems with optimal $H^{\infty}$ robustness performance. First, the Takagi and Sugeno fuzzy linear model is employed to approximate a nonlinear system. Next, based on the fuzzy linear model, a fuzzy controller is developed to stabilize the nonlinear system, and at the same time the effect of external disturbance on control performance is attenuated to a minimum level. Thus based on the fuzzy linear model, ��$H^{\infty}$ performance design can be achieved in nonlinear control systems. Linear matrix inequality (LMI) techniques are employed to solve this robust fuzzy control problem. PI control structure is used and the control gains are determined based on $H^{\infty}$ control.

• 한국생산제조학회지
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• 제22권5호
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• pp.845-851
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• 2013

In this paper, a robust optimization approach is applied to the two-axes stage using a piezoelectric actuator for precise motion tracking. Robust control is based on LQG/LTR (linear quadratic Gaussian control with loop transfer recovery) control. Further, an LMI (linear matrix inequality) is used to find the optimal parameter in the loop transfer recovery step, instead of a trial and error method. A decoupler in the shape of FIR filter is added to reduce the coupling effect between the motions of the two axes, and hence, the feedback control loop is designed independently for each axis motion. The experimental result shows that the proposed control scheme can be applied effectively for motion control of the two-axes stage.

• 제어로봇시스템학회논문지
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• 제10권3호
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• pp.216-224
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• 2004

In this paper we address reliable output feedback control problem for a class of linear systems with actuator/sensor failures. An output feedback control method is proposed which stabilizes the plant and guarantees $H_\inftyt$-norm constraint against all admissible actuator/sensor failures. The controller can be obtainer by solving some LMls that cover all failure cases. Effectiveness of this controller is validated via a numerical example. This paper addresses reliable output feedback control problem for a class of linear systems with actuator/sensor failures. An output feedback control method is proposed which stabilizes the plant and guarantees $H_\inftyt$-norm constraint against all admissible actuator/sensor failures. The controller can be obtained by solving some LMls that cover all failure cases. Effectiveness of this controller is validated via numerical example.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.11-18
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• 2003

The parametric approaches to sliding mode design are newly proposed for the class of multivariable systems. Our approach is based on an explicit formula for representing all the slid-ing modes using the Lyapunov matrices of full order. By manipulating Lyapunov matrices, the sliding modes which satisfy the design criteria such as the quadratic performance optimization and robust stability to parametric uncertainty, etc., can be easily obtained. The proposed ap-proach enables us to adopt a variety of Lyapunov- (or Riccati-) based approaches to the sliding mode design. Applications to the quadratic performance optimization problem, uncertain systems, systems with uncertain state delay, and the pole-clustering problem are discussed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.468-472
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• 2003

The lifting technique is a standard control procedure that is commonly applied to dual-rate systems, where a critical difficulty is that care must be taken so that the resulting equivalent system preserves the causality constraint between the control signal and the measured output. To overcome this difficulty, the most attractive result has been suggested by defining control time sequences as the union of sample and hold time sequences. However, the sacrifice of regular control period scheme results in some serious disadvantages; restrictions on the implementation to hardware and the corresponding inefficient control scheme. On the contrary, this paper proposes a novel dual-rate control technique, which redescribes the system as a control-rate-based system having regular control period and designs the controller, with no causality constraint, through Linear Matrix Inequality (LMI) formulation.

• 전기학회논문지
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• 제57권3호
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• pp.458-465
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• 2008

In this paper, we propose a non-fragile guaranteed cost controller design method for uncertain linear systems with constant delyas in state. The norm bounded and time-varying uncertainties are subjected to system and controller design matrices. A quadratic cost function is considered as the performance measure for the system. Based on the Lyapunov method, an LMI(Linear Matrix Inequality) optimization problem is established to design the controller which uses information of delayed state and minimizes the upper bound of the quadratic cost function for all admissible system uncertainties and controller gain variations. Numerical examples show the effectiveness of the proposed method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2564-2566
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• 2005

This paper presents an iterative linear matrix inequality (LMI) method for $H_2$ and $H_{\infty}$ optimal static output feedback (SOF) control, which is expressed in terms of LMIs subject to an additional rank condition. We propose a linear Penalty function to penalize the rank constraint so that static $H_2$ and $H_{\infty}$ synthesis results in solving a series of convex LMI optimization problems. Numerical experiments for various $H_2$ and $H_{\infty}$ SOF synthesis were performed to demonstrate the effectiveness of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.685-689
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• 2005

In this paper, we consider a robust output feedback model predictive controller(MPC) design for Wiener model. Nonlinearities that couldn't be represented in static nonlinearity block of Wiener model are regarded as uncertainties in linear block. An dynamic output feedback controller design method is presented for Wiener MPC. According to MPC algorithm, the control law is computed based on linear matrix inequality(LMI)at each sampling time by solving convex optimization. Also, a new parameter dependent Lyapunov function is proposed to get a less conservative condition. The results are illustrated with numerical example.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2242-2244
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• 2004

This paper deals with the design of a reduced-order stabilizing controller for the linear system. The coupled lineal matrix inequality (LMI) problem subject to a rank condition is solved by a sequential semidefinite programming (SDP) approach. The nonconvex rank constraint is incorporated into a strictly linear penalty function, and the computation of the gradient and Hessian function for the Newton method is not required. The penalty factor and related term are updated iteratively. Therefore the overall procedure leads to a successive LMI relaxation method. Extensive numerical experiments illustrate the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.169.3-169
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• 2001

This paper introduces a new approach to analysis of error convergence for a class of iterative learning control systems. First, a nonlinear plant is represented using a Takagi-Sugeno(T-S) fuzzy model. Then each iterative learning controller is designed for each linear plant in the T-S fuzzy model. From the view point of two-dimensional(2-D) system theory, we transform the proposed learning systems to a 2-D error equation, which is also established in the form of T-S fuzzy model. We analysis the error convergence in the sense of induced 2 L -norm, where the effects of disturbances and initial conditions on 2-D error are considered. The iterative learning controller design problem to guarantee the error convergence can be reduced to linear matrix inequality problems. In comparison with others, our learning algorithm ...