A Course In Robust Control Theory by Geir E. Dullerud

By Geir E. Dullerud

Throughout the 90s strong regulate concept has visible significant advances and completed a brand new adulthood, established round the idea of convexity. The aim of this e-book is to offer a graduate-level path in this idea that emphasizes those new advancements, yet whilst conveys the most ideas and ubiquitous instruments on the center of the topic. Its pedagogical goals are to introduce a coherent and unified framework for learning the speculation, to supply scholars with the control-theoretic historical past required to learn and give a contribution to the study literature, and to give the most rules and demonstrations of the main effects. The e-book can be of worth to mathematical researchers and desktop scientists, graduate scholars planning on doing study within the sector, and engineering practitioners requiring complex regulate innovations.

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E. v1 v2 2 C implies v1 + v2 2 C ). A canonical example of a convex cone is a half-space F (v ) 0 that goes through the origin, or an intersection of half-spaces of this form. 6. 6. Convex and nonconvex cones Up to this point we have introduced a number of concepts related to vector spaces and have for the most part developed our results in a basis independent way, emphasizing the structure of linear spaces. This provides a powerful way to visualize linear algebra. However for most parts of the course it is much more e cient to work in a particular basis, especially since we are interested in developing results with computation in mind.

10. Show that if P 0 and Q 0, then Tr(PQ) 0. 11. Suppose A 2 C m n has the singular value decomposition U V . (a) Show that if x 2 spanfvk+1 : : : vn g then jAxj k+1 jxj. (b) If A is n n, let 1 2 n be the diagonal elements of . Denote (A) = n . Show that A is invertible if and only if (A) > 0, and in that case (A;1 ) = (1A) : (c) If A 2 H n , then ; (A) I A (A) I . 12. Suppose that > 0 and that X 2 Rn m . (a) Show that (X ) if and only if X X 2 (b) Convert the constraint (X ) to an equivalent LMI condition.

In the next section we will pursue some of the geometric properties of sets in linear spaces. 2 Subsets and Convexity Up to now in this chapter, the only sets we have encountered are those that are also vector spaces. In this section we will consider more general subsets of a vector space V , with an emphasis of introducing and examining convexity. Convexity is one of the main geometric ideas underlying much of global optimization theory, and in particular is the foundation for a major analytical tool used in this course.

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