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 historical past that has principally been software pushed. This publication unifies those buildings in a accomplished and obtainable means, drawing from the most recent tools and functions to teach how they come up certainly within the thought of linear keep an eye on structures. Magnus Egerstedt and Clyde Martin are best innovators within the use of keep an eye on theoretic splines to compile many assorted functions inside a typical framework. during this e-book, 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 reveal how all of those difficulties are a part of a similar basic mathematical framework, and the way they're all, to a undeniable measure, a outcome of the optimization challenge of discovering the shortest distance from some extent to an affine subspace in a Hilbert area. They disguise periodic splines, monotone splines, and splines with inequality constraints, and clarify how any finite variety of linear constraints might be further. This publication unearths how the various average connections among regulate conception, numerical research, and information can be utilized to generate strong mathematical and analytical tools.This ebook is a wonderful source for college students and pros on top of things idea, robotics, engineering, special effects, econometrics, and any zone that calls for the development of curves in line with units of uncooked information.

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

Example text

The theory of smoothing splines is based on the premise that a datum, α, is the sum of a deterministic part, β, and a random part, . It is assumed that is the value of a random variable drawn from some probability distribution. Smoothing splines are designed to approximate the deterministic part by minimizing the variance of the random part. Often the random variable comes from measurement error. We start this chapter with two examples in which the random error comes either from the measurements or from estimations based on incomplete data.

N }. Hence, consider a solution of the form u0 (t) = N τi ti (t) i=1 and evaluate T (u0 ) to obtain ⎛ N wi ti (t)Lti ⎝ i=1 ⎞ N τj tj (t)⎠ + ρ j=1 N τi ti (t) = 0. i=1 Thus, for each i, N Lti ( wi tj )τj + ρτi = 0. j=1 The coefficient τ (τ T = (τ1 , . . , τN )T ) is then the solution of a set of linear equations of the form (W G + ρI)τ = 0, where W is the diagonal matrix of the weights wi and G = [gij ] is, as before, the Grammian with gij = Lti ( tj ). Now, consider the matrix W G + ρI and multiply it on the left by W−1 , and consider the scalar z T (G + ρW −1 )z = z T Gz + ρz T W −1 z > 0, since both G and ρW −1 are positive definite.

N , the system of linear equations (W G+ρI)τ = W α. 3, the coefficient matrix is invertible, and hence the solution exists and is unique. The resulting curve y(t) is a spline. The major difference between classic, interpolating splines and smoothing splines is that the nodal points are determined by the optimization instead of being predetermined. It should, moreover, be noted that inverting the matrix W G + ρI is not trivial. Since it is a Grammian, we can expect it to be badly conditioned. However, by using the techniques in [30], the conditioning can be improved.

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