• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.111-114
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• 2003

In this paper, we consider the stabilization and $H_\infty$ control of linear systems with static output feedback control. The static output feedback control represents the simplest closed-loop control that can be realized in practice, and, moreover, it is less expensive to be implemented and is more reliable. In spite of its advantages, it is one of the open problems which is not sloved analytically or numerically yet. After decompose the closed-loop system into feedback form, by adopting the small gain theorem, we obtain a sufficient condition for stabilization and a sufficient condition for It control expressed as linear matrix inequalites. Finally, we show the usefulness of our results by a numerical example.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.907-915
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• 2002

We present a state-space approach to design a passivity-based dynamic output feedback control of a finite collection of non-square linear systems. We first determine a squaring gain matrix and an additional dynamics that is connected to the systems in a feedforward way, then a static passivating (i.e. rendering passive) control law is designed. Consequently, the actual feedback controller will be the static control law combined with the feedforward dynamics. A necessary and sufficient condition for the existence of the parallel feedfornward compensator (PFC) is given by the static output feedback fomulation, which enables to utilize linear matrix inequality (LMI). The effectiveness of the proposed method is illustrated by some examples including the systems which can be stabilized by the proprotional-derivative (PD) control law.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.31-39
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• 2000

We present a robust and reliable H$\infty$ state-feedback controller design for linear uncertain systems, which have norm-bounded time-varying uncertainty in the state matrix, and their prespecified sets of actuators are susceptible to failure. These controllers should guarantee robust stability of the systems and H$\infty$ norm bound against parameter uncertainty and/or actuator failures. Based on the linear matrix inequality (LMI) approach, two state-feedback controller design methods are constructed by formulating to a set of LMIs corresponding to all failure cases or a single LMI that covers all failure cases, with an additional costraint. Effectiveness and geometrical property of these controllers are validated via several numerical examples. Furthermore, the proposed LMI frameworks can be applied to multiobjective problems with additional constraints.

• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1608-1615
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• 2003

This paper studies on the linearization of a looper system in hot strip mills, that plays an important role in regulating a strip tension or a strip width. Nonlinear dynamic equations of the looper system are analytically linearized by a static feedback linearization algorithm with a compensator. The proposed linear model of the looper is validated by a comparison with a linear model using Taylor's series. It is shown that the linear model by static feedback well describes nonlinearities of the looper system than one using Taylor's series. Furthermore, it is shown from the design of an ILQ controller that the linear model by static feedback is very useful in designing a linear controller of the looper system.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.915-920
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• 2007

A new optimal state feedback and disturbance feedforward control design in the sense of minimizing $L_{2}-gain$ from disturbance to control output is proposed for disturbance attenuation of systems with bounded control input and measurable disturbance. The controller is derived in the framework of linear matrix inequality(LMI) optimization. A gain scheduled state feedback and disturbance feedforward control design is also suggested to improve disturbance attenuation performance. The control gains are scheduled according to the proximity to the origin of the state of the plant and the magnitude of disturbance. This procedure yields a stable linear time varying control structure that allows higher gain and hence higher performance controller as the state and the disturbance move closer to the origin. The main results give sufficient conditions for the satisfaction of a parameter-dependent performance measure, without violating the bounded control input condition.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.11
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• pp.59-65
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• 2007

A new optimal state feedback and disturbance feedforward control design in the sense of minimizing $L_2$-gain from disturbance to control output is proposed for disturbance attenuation of systems with bounded control input and measurable disturbance. The controller is derived in the framework of linear matrix inequality(LMI) optimization. A gain scheduled state feedback and disturbance feedforward control design is also suggested to improve disturbance attenuation performance. The control gains are scheduled according to the proximity to the origin of the state of the plant and the magnitude of disturbance. This procedure yields a stable linear time varying control structure that allows higher gain and hence higher performance controller as the state and the disturbance move closer to the origin. The main results give sufficient conditions for the satisfaction of a parameter-dependent performance measure, without violating the bounded control input condition.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.56-69
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• 2005

In this paper, it is clearly shown that the two well-known necessary and sufficient conditions mp n as generic static output feedback pole-assignment and mp + d(m+p) n+d as generic minimum d-th order dynamic output feedback pole-assignment on complex field, unbelievably, do not match up each other in strictly proper linear systems. For the analysis, a diagram analysis is newly created (which is defined by the analysis of 'convoluted rectangular/dot diagrams' constructed via node-branch conversion of the signal flow graphs of output feedback gain loops). Under this diagram analysis, it is proved that the minimum d-th order dynamic output feedback compensator for pole-assignment in m-input, p-output, n-th order systems is quantitatively decomposed into static output feedback compensator and its associated d number of arbitrary 1st order dynamic elements in augmented (m+d)-input, (p+d)-output, (n+d)-th order systems. Total configuration of the mismatched data is presented in a Table.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2672-2674
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• 2000

In this paper. feedback linearization and the sliding mode control(SMC) are used together for uncertain nonlinear system. An advantage of feedback linearization technique is to make linear control theories can be used for nonlinear system and the SMC have the robustness. But the dynamics of the SMC has the dynamics lower order than that of the original system. Therefore the linear control theory can not be used with the SMC. The novel sliding surface of the SMC can have the dynamics of the nominal non linear system controlled by the feedback linearization. The proposed method can be used for the control of induction motors.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.2
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• pp.76-80
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• 2005

In this paper, a Direct Learning Control (DLC) method is proposed for linear feedback systems to improve the tracking performance when the task of the control system is repetitive. DLC can generate the desired control input directly from the previously learned control inputs corresponding to other output trajectories. It is assumed that all the desired output functions given to the system have some relations called proportionality and it is shown by mathematical analysis that DLC can be utilized to genera additional control efforts for the perfect tracking. To show the validity and tracking performance of the proposed method, some simulations are performed for the tracking control of a linear system with a PI controller.