• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.24-34
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• 2008

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.397-402
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• 2005

The guaranteed cost control problem for a class of linear systems with norm-bounded time-varying parameter uncertainties and input constraints is considered. A sufficient condition for the existence of guaranteed cost state feedback controllers is derived via the linear matrix inequality (LMI) approach, and a design procedure to guaranteed cost controllers is given. Furthermore, a convex optimization problem is formulated to determine the optimal guaranteed cost controller. An example is given to illustrate the effectiveness of the proposed results.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.10-15
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• 2004

We present a state-space approach to make non-square linear systems strictly passive by using an input-dimensional parallel feedforward compensator. A necessary and sufficient condition for the existence of the parallel feedforward compensator is given by the static output feedback formulation, which enables to utilize linear matrix inequality. By modifying the structure of the compensator the additional technical assumption in the previous result [1] is removed. The effectiveness of the proposed method is illustrated by some numerical examples which can be stabilized by the proportional-derivative (PD) and proportional-derivative-integral (PID) control laws. The proposed control scheme can successfully replace the measurements of derivative terms in the control laws.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.2
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• pp.140-144
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• 2010

In the case of a linear time-varying system, it is difficult to apply the conventional stability conditions of RHC (Receding Horizon Control) to real physical systems because of computational complexity comes from time-varying system and backward Riccati equation. Therefore, in this study, a frozen time RHC for a linear discrete time-varying system is proposed. Since the proposed control law is obtained by time-invariant Riccati equation solved by forward iterations at each control time, its stability can be ensured by matrix inequality condition and the stability condition based on horizon for a time-invariant system, and they can be applied to real physical systems effectively in comparison with the conventional RHC.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.5
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• pp.26-33
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• 2002

The design method of robust $H_{\infty}$ filtering for discrete-time uncertain linear systems is investigated in this paper. The uncertain parameters are assumed to be unknown but belonging to known convex compact set of polytope type. The objective is to design a stable robust $H_{\infty}$ filter guaranteeing the asymptotic stability of filtering error dynamics and present an $L_2$ induced norm bound analytically for the modified $H_{\infty}$ performance measure. The sufficient condition for the existence of robust $H_{\infty}$ filter and the filter design method are established by LMI(linear matrix inequality) approach, which can be solved efficiently by convex optimization. The proposed algorithm is checked through an example.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.506-511
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• 2006

This paper considers a method to design sliding mode observers for a class of uncertain systems using Linear Matrix Inequalities(LMI). In an LMI-based sliding mode observer design method for a class of uncertain systems the switching surface is set to be the difference between the observer and system output. In terms of LMIs, a necessary and sufficient condition is derived for the existence of a sliding-mode observer guaranteeing a stable sliding motion on the switching surface. The gain matrices of the sliding-mode observer are characterized using the solution of the LMI existence condition. The results are illustrated by an example.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.5
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.3
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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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• v.1 no.1
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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.

• Proceedings of the KIEE Conference
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• 2004.07d
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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.