• 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.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.3
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• pp.32-39
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• 2009

A new discrete time gain-scheduled control design is proposed to improve disturbance attenuation for systems with bounded control input under known disturbance maximum norm. The state feedback gains are scheduled according to the proximity of the state of the plant to the origin. The controllers are derived in the framework of linear matrix inequality(LMI) optimization. This procedure yields a linear time varying control structure that allows higher gain and hence higher performance controllers as the state moves 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 under the given disturbance maximum norm.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.6 s.183
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• pp.81-87
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• 2006

A new gain-scheduled control design is proposed to improve disturbance attenuation for systems with bounded control input. The state feedback controller is scheduled according to the proximity to the origin of the state of the plant. The controllers is derived in the framework of linear matrix inequality(LMI) optimization. This procedure yields a linear time varying control structure that allows higher gain and hence higher performance controllers as the state 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.89-90
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• 2006

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.5
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• pp.503-510
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• 2004

In this paper, the well-conditioned observer for a stochastic system is designed so that the observer is less sensitive to the ill-conditioning factors in transient and steady-state observer performance. These factors include not only deterministic uncertainties such as unknown initial estimation error, round-off error, modeling error and sensing bias, but also stochastic uncertainties such as disturbance and sensor noise. In deterministic perspectives, a small value in the L$_{2}$ norm condition number of the observer eigenvector matrix guarantees robust estimation performance to the deterministic uncertainties. In stochastic viewpoints, the estimation variance represents the robustness to the stochastic uncertainties and its upper bound can be minimized by reducing the observer gain and increasing the decay rate. Both deterministic and stochastic issues are considered as a weighted sum with a LMI (Linear Matrix Inequality) formulation. The gain in the well-conditioned observer is optimally chosen by the optimization technique. Simulation examples are given to evaluate the estimation performance of the proposed observer.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.21-26
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• 2003

The well-conditioned observer in a stochastic system is designed so that the observer is less sensitive to the ill-conditioning factors in transient and steady-state observer performance. These factors include not only deterministic issues such as unknown initial estimation error, round-off error, modeling error and sensing bias, but also stochastic issues such as disturbance and sensor noise. In deterministic perspectives, a small value in the L$_2$ norm condition number of the observer eigenvector matrix guarantees robust estimation performance to the deterministic issues and its upper bound can be minimized by reducing the observer gain and increasing the decay rate. Both deterministic and stochastic issues are considered as a weighted sum with a LMI (Linear Matrix Inequality) formulation. The gain in the well-conditioned observer is optimally chosen by the optimization technique. Simulation examples are given to evaluate the estimation performance of the proposed observer.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.494-496
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• 1999

In this paper, we consider the design of a state feedback $H_{\infty}$ controller for uncertain linear systems with saturating actuators. We consider a general saturating actuator and employ the additive decomposition to deal with it effectively. And the considered uncertainty is the unstructured uncertainty which is only known its norm bound. Based on Linear Matrix Inequality(LMI) techniques, we present a condition on designing a controller that guarantees the $L_2$ gain, from the noise to the output, is not greater than a given value. A controller is obtained by checking the feasibility of three LMI's, and this can be easily done by well-known control package. Finally, we show the usefulness of our result by a numerical example.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.596-598
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• 1999

In this paper, we consider the design of a state feedback $H_{\infty}$ controller for linear systems with saturating actuators. We consider a general saturating actuator and employ the multiplicative decomposition to deal with it effectively. Based on Linear Matrix Inequality (LMI) techniques, we present a condition on designing a controller that guarantees the $L_2$ gain, from the noise to the output, is not greater than a given value. A controller is obtained by checking the feasibility of three LMI's, and this can be easily done by well-known control package. Finally, we show the usefulness of our result by a numerical example.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.4
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• pp.191-200
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• 2000

In practical applications, we frequently encounter the actuator nonlinearity in control systems, and its representative nonlinearity is saturation. A controller designed without considering this saturation nonlinearity is often a source of degradation of performance. To treat the saturation nonlinearity more efficiently, we adopt the multiplicative decomposition and the additive decomposition. Based on these decompositions, we present two controller design methods in the LMI(Linear Matrix Inequality) form that guarantee the L2 gain, from the disturbance to the measured output, is less than or equal to a given value. Finally, we give two examples to show the applicability and usefulness of our results.