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Linear parametrically varying systems with brief instabilities: an application to integrated vision/IMU navigation
 IEEE Trans. on Aerospace and Electronics Systems
, 2001
"... This paper addresses the problem of nonlinear filter design to estimate the relative position and velocity of an unmanned air vehicle (UAV) with respect to a point on a ship using infrared (IR) vision, inertial, and air data sensors. Sufficient conditions are derived for the existence of a particula ..."
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Cited by 22 (10 self)
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This paper addresses the problem of nonlinear filter design to estimate the relative position and velocity of an unmanned air vehicle (UAV) with respect to a point on a ship using infrared (IR) vision, inertial, and air data sensors. Sufficient conditions are derived for the existence of a particular type of complementary filters with guaranteed stability and performance in the presence of socalled outofframe events that arise when the vision system loses its target temporarily. The results obtained build upon new developments in the theory of linear parametrically varying systems (LPVs) with brief instabilities–also reported in the paper–and provide the proper framework to deal with outofframe events. Field tests with a prototype UAV illustrate the performance of the filter and the scope of applications of the new theory developed. Manuscript received January 10, 2003; revised December 14, 2003; released for publication April 12, 2004.
PWLTLOOL: A Matlab toolbox for analysis of Piecewise Linear Systems
, 1999
"... Introduction This manual describes a MATLAB toolbox for computational analysis of piecewise linear systems. Key features of the toolbox are modeling, simulation, analysis, and optimal control for piecewise linear systems. The simulation routines detect sliding modes and simulate equivalent dynamics ..."
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Introduction This manual describes a MATLAB toolbox for computational analysis of piecewise linear systems. Key features of the toolbox are modeling, simulation, analysis, and optimal control for piecewise linear systems. The simulation routines detect sliding modes and simulate equivalent dynamics [2]. The analysis and design are based on computation of piecewise quadratic Lyapunov functions [6]. The computations are performed using convex optimization in terms of linear matrix inequalities (LMIs). This version of the toolbox requires the LMI control toolbox The structure of this manual is as follows. Section 2 describes the model repre sentation, i.e. how a piecewise linear (PWL) system is defined in this toolbox. Certain structures of the PWL systems allow the systems to be defined in a more automated fashion. These systems, in the sequel referred to as Structured PWL (sPWL) systems, are handled by an additional set of commands described in Section 3. Section 4 lists all the comma
ISSN 0280–5316 ISRN LUTFD2/TFRT7582SE PWLT L A Matlab toolbox for analysis of Piecewise Linear Systems
"... x2 � 2 x2 �−2x1−4 X1 x1 x2 �−2x1+4 x2 �−2 Figure 1 Example of a polyhedron in R 2. 1. ..."
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x2 � 2 x2 �−2x1−4 X1 x1 x2 �−2x1+4 x2 �−2 Figure 1 Example of a polyhedron in R 2. 1.