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E-raamat: Control Theoretic Splines: Optimal Control, Statistics, and Path Planning

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"This book is an excellent resource for students and professionals in control theory, robotics, engineering, computer graphics, econometrics, and any area that requires the construction of curves based on sets of raw data."--BOOK JACKET.

Splines, both interpolatory and smoothing, have a long and rich history that has largely been application driven. This book unifies these constructions in a comprehensive and accessible way, drawing from the latest methods and applications to show how they arise naturally in the theory of linear control systems. Magnus Egerstedt and Clyde Martin are leading innovators in the use of control theoretic splines to bring together many diverse applications within a common framework. In this book, they begin with a series of problems ranging from path planning to statistics to approximation. Using the tools of optimization over vector spaces, Egerstedt and Martin demonstrate how all of these problems are part of the same general mathematical framework, and how they are all, to a certain degree, a consequence of the optimization problem of finding the shortest distance from a point to an affine subspace in a Hilbert space. They cover periodic splines, monotone splines, and splines with inequality constraints, and explain how any finite number of linear constraints can be added. This book reveals how the many natural connections between control theory, numerical analysis, and statistics can be used to generate powerful mathematical and analytical tools.This book is an excellent resource for students and professionals in control theory, robotics, engineering, computer graphics, econometrics, and any area that requires the construction of curves based on sets of raw data.

Arvustused

"[ T]he book presents an excellent treatment of the topic of control-theoretic splines. It showcases effective and transparent use of optimization technique in function space sellings and of optimal control techniques to problems in the domain of approximation theory."--Ilya Kolmanovsky, Mathematical Reviews

Muu info

This is the only book I know of that combines control theory with splines. Its multidisciplinary approach will appeal to a wide range of readers, including researchers in control theory and splines, numerical analysis, engineering, and biology. The book is well organized and nicely written. Reading it was quite enjoyable. -- Zhimin Zhang, Wayne State University
Preface ix
Chapter
1. INTRODUCTION
1
1.1 From Interpolation to Smoothing
1
1.2 Background
2
1.3 The Introduction of Control Theory
4
1.4 Applications
7
1.5 Topical Outline of the Book
8
Chapter
2. CONTROL SYSTEMS AND MINIMUM NORM PROBLEMS
11
2.1 Linear Control Systems
11
2.2 Hilbert Spaces
14
2.3 The Projection Theorem
15
2.4 Optimization and Gateaux Derivatives
18
2.5 The Point-to-Point Transfer Problem
21
Chapter
3. EIGHT FUNDAMENTAL PROBLEMS
25
3.1 The Basic Set-Up
26
3.2 Interpolating Splines
29
3.3 Interpolating Splines with Constraints
31
3.4 Smoothing Splines
35
3.5 Smoothing Splines with Constraints
38
3.6 Dynamic Time Warping
45
3.7 Trajectory Planning
48
Chapter
4. SMOOTHING SPLINES AND GENERALIZATIONS
53
4.1 The Basic Smoothing Problem
56
4.2 The Basic Algorithm
60
4.3 Interpolating Splines with Initial Data
62
4.4 Problems with Additional Constraints
63
Chapter
5. APPROXIMATIONS AND LIMITING CONCEPTS
73
5.1 Basic Assumptions
73
5.2 Convergence of the Smoothing Spline
75
5.3 Quadrature Schemes
80
5.4 Rate of Convergence
82
5.5 Cubic Spline Convergence Bounds
83
Chapter
6. SMOOTHING SPLINES WITH CONTINUOUS DATA
87
6.1 Continuous Data
89
6.2 The Continuous Smoothing Problem
89
6.3 The Basic Two-Point Boundary Value Problem
91
6.4 The General Two-Point Boundary Value Problem
95
6.5 Multipoint Problems
99
6.6 Recursive Splines
101
Chapter
7. MONOTONE SMOOTHING SPLINES
113
7.1 The Monotone Smoothing Problem
113
7.2 Properties of the Solution
115
7.3 Dynamic Programming
118
7.4 Monotone Cubic Splines
120
7.5 Probability Densities
126
Chapter
8. SMOOTHING SPLINES AS INTEGRAL FILTERS
133
8.1 Smoothing Concepts
133
8.2 Splines from Statistical Data
136
8.3 The Optimal Control Problem
141
8.4 The Cubic Smoothing Spline
146
Chapter
9. OPTIMAL TRANSFER BETWEEN AFFINE VARIETIES
155
9.1 Point-to-Point Transfer
155
9.2 Transfer between Affine Varieties
156
9.3 Transfer through Dynamic Programming
158
9.4 A Multi-Agent Problem
164
Chapter
10. PATH PLANNING AND TELEMETRY
169
10.1 The Telemetry Problem
169
10.2 Splines on Spheres
171
10.3 Splines and Bezier Curves
176
10.4 Conflict Resolution for Autonomous Vehicles
185
Chapter
11. NODE SELECTION
193
11.1 Background
193
11.2 Sampling for Interpolation and Smoothing
194
11.3 Optimal Timing Control
195
11.4 Applications to Smoothing Splines
199
Bibliography 205
Index 215
Magnus Egerstedt is associate professor of electrical and computer engineering at Georgia Institute of Technology. Clyde Martin is the P. W. Horn Professor of Mathematics and Statistics at Texas Tech University.