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LMI-based robust control of fractional-order uncertain linear systems [PDF]
Computers and Mathematics With Applications, 2011 This paper concerns with the method of observer-based control and static output feedback control for fractional-order uncertain systems with the fractional commensurate order α ...
Yong-Hong Lan, Yong Zhou
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LMI Relaxations in Robust Control
European Journal of Control, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Manufacturing Systems: LMI Approach
IEEE Transactions on Automatic Control, 2006This note deals with the control of production systems that produce many part types with limited capacity. First, a simple model is used to show that the inventory control problem can be solved using modern control theory. A state feedback controller that forces the cumulative production of the system to track precisely the cumulative demand is ...
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Strong stabilisation: an LMI approach
International Journal of Modelling, Identification and Control, 2007In this paper, the Strong Stabilisation Problem (StSP) of proper and non-minimum phase linear time-invariant systems is considered. The formulation of the problem resulted in a sufficient condition in a form of Linear Matrix Inequality (LMI) which will be solved using MATLAB LMI Toolbox.
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Rank-one LMIs and Lyapunov's inequality
IEEE Transactions on Automatic Control, 2001The paper proposes an alternative proof of Lyapunov's matrix inequality about the location of the eigenvalues of a matrix in some region of the complex plane. This new proof does not refer to stability of the trajectories of an associated dynamical system and does not use matrix exponentials.
Didier Henrion, Gjerrit Meinsma
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Proceedings of 1994 33rd IEEE Conference on Decision and Control, 2002
This paper describes a new MATLAB-based toolbox for control design via linear matrix inequality (LMI) techniques. After a brief review of LMIs and of some of their applications to control, the toolbox contents and capabilities are presented. >
P. Gahinet +3 more
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This paper describes a new MATLAB-based toolbox for control design via linear matrix inequality (LMI) techniques. After a brief review of LMIs and of some of their applications to control, the toolbox contents and capabilities are presented. >
P. Gahinet +3 more
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2003
This chapter provides a brief introduction to charged particle optics. It is not meant to be a survey; rather, our intent is to provide enough information to uynderstand how the optical system of focused ion beam system works. We also provide a method to define the resolution of a focused ion beam system in a way that gives a metric for optimization or
Jon Orloff, Mark Utlaut, Lynwood Swanson
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This chapter provides a brief introduction to charged particle optics. It is not meant to be a survey; rather, our intent is to provide enough information to uynderstand how the optical system of focused ion beam system works. We also provide a method to define the resolution of a focused ion beam system in a way that gives a metric for optimization or
Jon Orloff, Mark Utlaut, Lynwood Swanson
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IFAC Proceedings Volumes, 2002
Abstract In this work, we present a Model Predictive Controller (MPC) based on Linear Matrix Inequalities (LMI's). As in the standard MPC algorithms, at each (sampling) time, a convex optimization problem is solved to compute the control law. The optimization involves constraints written as LMI's, including those normally associated to MPC problems ...
Ernesto Granado +3 more
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Abstract In this work, we present a Model Predictive Controller (MPC) based on Linear Matrix Inequalities (LMI's). As in the standard MPC algorithms, at each (sampling) time, a convex optimization problem is solved to compute the control law. The optimization involves constraints written as LMI's, including those normally associated to MPC problems ...
Ernesto Granado +3 more
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2019
In this chapter, following an introduction on the fundamentals of linear matrix inequalities (LMIs), the application of LMIs to solve convex optimization problems, using numerical examples, is explained. Then, the robust optimization problems are formulated and solved via the LMI-based H∞ and mixed H2∕H∞ optimization techniques.
Mohammad Fathi, Hassan Bevrani
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In this chapter, following an introduction on the fundamentals of linear matrix inequalities (LMIs), the application of LMIs to solve convex optimization problems, using numerical examples, is explained. Then, the robust optimization problems are formulated and solved via the LMI-based H∞ and mixed H2∕H∞ optimization techniques.
Mohammad Fathi, Hassan Bevrani
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Robust LMIs with polynomial dependence on the uncertainty
2007 46th IEEE Conference on Decision and Control, 2007Solving robust linear matrix inequalities (LMIs) has long been recognized as an important problem in robust control. Although the solution to this problem is well-known for the case of affine dependence on the uncertainty, to the best of our knowledge, results for other types of dependence are limited.
F Dabbene, C Feng, C Lagoa
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