Quadratic Integral Penalty Methods for Numerical Trajectory Optimization
Martin Peter Neuenhofen

TL;DR
This thesis introduces quadratic integral penalty algorithms for solving dynamic optimization problems, emphasizing convergence, avoiding numerical artifacts, and demonstrating effectiveness through computational experiments.
Contribution
It proposes three novel methods for dynamic optimization, including a direct transcription with quadratic penalties, a modified augmented Lagrangian, and a combined penalty and barrier approach.
Findings
Methods effectively solve dynamic optimization problems.
Algorithms avoid numerical artifacts like ringing.
Convergence is rigorously proven and demonstrated numerically.
Abstract
This thesis presents new mathematical algorithms for the numerical solution of a mathematical problem class called \emph{dynamic optimization problems}. These are mathematical optimization problems, i.e., problems in which numbers are sought that minimize an expression subject to obeying equality and inequality constraints. Dynamic optimization problems are distinct from non-dynamic problems in that the sought numbers may vary over one independent variable. This independent variable can be thought of as, e.g., time. This thesis presents three methods, with emphasis on algorithms, convergence analysis, and computational demonstrations. The first method is a direct transcription method that is based on an integral quadratic penalty term. The purpose of this method is to avoid numerical artifacts such as ringing or erroneous/spurious solutions that may arise in direct collocation…
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Taxonomy
TopicsAerospace Engineering and Control Systems
