Download Control Theoretic Splines: Optimal Control, Statistics, and by Magnus Egerstedt PDF

By Magnus Egerstedt

Splines, either interpolatory and smoothing, have a protracted and wealthy heritage that has principally been program pushed. This booklet unifies those structures in a accomplished and obtainable method, drawing from the most recent equipment and functions to teach how they come up evidently within the idea of linear regulate structures. Magnus Egerstedt and Clyde Martin are best innovators within the use of keep watch over theoretic splines to compile many assorted functions inside a typical framework. during this booklet, they start with a sequence of difficulties starting from direction making plans to stats to approximation. utilizing the instruments of optimization over vector areas, Egerstedt and Martin show how all of those difficulties are a part of an identical common mathematical framework, and the way they're all, to a definite measure, a end result of the optimization challenge of discovering the shortest distance from some extent to an affine subspace in a Hilbert house. They hide periodic splines, monotone splines, and splines with inequality constraints, and clarify how any finite variety of linear constraints may be further. This publication finds how the numerous traditional connections among keep an eye on thought, numerical research, and facts can be utilized to generate strong mathematical and analytical tools.

This ebook is a superb source for college kids and execs on top of things conception, robotics, engineering, special effects, econometrics, and any quarter that calls for the development of curves in line with units of uncooked data.

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Additional resources for Control Theoretic Splines: Optimal Control, Statistics, and Path Planning (Princeton Series in Applied Mathematics)

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That construction develops the banded structure directly, but has the disadvantage of not carrying over to the more general problems that are pursued in this book. 2 Example As an example, consider the classic cubic, interpolating splines, where y¨(t) = u(t), that is, where A= 0 1 0 0 , b= 0 1 , c= 1 0 . 1, where the paramaters used are T = 1, N = 4, t1 = 1/4, t2 = 1/2, t3 = 3/4, t4 = 1, α1 = 3/4, α2 = 2/5, α3 = 1/4, α4 = 1. 3 INTERPOLATING SPLINES WITH CONSTRAINTS In this section, we consider a somewhat different problem that arises in a number of applications and that can be solved in much the same manner as EditedFinal September 23, 2009 32 CHAPTER 3 for the classical interpolating spline.

N )T , λT = (λ1 , λ2 , . . , λN )T . We first minimize the function H over u, assuming that λ and γ are fixed. This minimum is achieved at the point where the Gateaux derivative of H, with respect to u, is zero. This is found by calculating 1 lim (H(u + v, λ, γ) − H(u, γ, λ)) →0 = T 0 u(t) − N λi i=1 ti (t) + N γi i=1 ti (t) v(t)dt. 18) i=1 where, as before, (t)T = ( T t1 (t), t2 (t), . . , tN (t)) . We now eliminate u from H to obtain H(u , λ, γ) = 1 2 T 0 N ((λT − γ T ) (t))2 dt + λi (ai − Lti (u )) − i=1 N γi (Lti (u ) − bi ) i=1 N N 1 = (λ − γ)T G(λ − γ) + λT a − γ T b − λi Lti (u ) + γi Lti (u ) 2 i=1 i=1 N N 1 T T T = (λ − γ) G(λ − γ) + λ a − γ b − λi λj Lti ( 2 i=1 j=1 + N N λj γi Lti ( i=1 j=1 tj ) + N N λi γj Lti ( tj ) − i=1 j=1 N N tj ) γj γi Lti ( tj ) i=1 j=1 1 = (λ − γ)T G(λ − γ) + λT a − γ T b − λT Gλ + λT Gγ + λT Gγ − γ T Gγ 2 1 = (λ − γ)T G(λ − γ) + λT a − γ T b − (λ − γ)T G(λ − γ) 2 1 = − (λ − γ)T G(λ − γ) + λT a − γ T b.

0 0 .. 4) . 3) is equivalent to a system on this form. For example, consider the problem of controlling a linear spring connected to a unit mass. If we let y be the position of the mass, and let u be an externally applied force, then Newton’s Second Law dictates that y¨(t) = −δy(t) ˙ − ky(t) + u(t), where k is the spring coefficient and δ is the damping coefficient. Now, setting x1 = y, x2 = y, ˙ and x = (x1 , x2 )T , we get x(t) ˙ = y(t) = 0 1 x(t) + −k −δ 1 0 x(t), 0 1 u(t), which is of the prescribed form.

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