Numerical optimization and optimal control for dynamical systems [146442]
Lecture Notes
Contents
These notes develop the mathematical and numerical tools used to model, analyse, and optimize dynamical systems. The mathematical material is organized in five parts: constrained optimization and Karush–Kuhn–Tucker conditions (KKT), ordinary differential equations (ODE), differential-algebraic equations (DAE), functional minimization, and optimal control problems (OCP). A sixth part collects the reproducible Python laboratories.
Exercises, worked examples and case studies are collected in a companion volume, Exercises and worked examples. What remains here are only the examples that carry the exposition forward. The algorithms, their order and stability analysis and the Python laboratories are in a third volume, Numerical Methods.
The presentation moves from differential models and their numerical integration to algebraic constraints and variational methods, providing the foundations required for the formulation and solution of optimal control problems.
- Constrained Minimization
- Ordinary Differential Equations
- Differential-Algebraic Equations
- Calculus of Variations
- Optimal Control
- References
Acknowledgement
This project is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0). Some pages may contain materials that are subject to copyright, in which case you will see the copyright notice.
