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Control Theory and State Estimation

Lecture notes on Control Theory and Estimation based on a class I taught in the Department of Mathematics at Ohio University.

Control theory combines rigorous mathematics of optimization, differential equations, dynamical systems, linear algebra, calculus of variations, probability, and other more theoretical areas of mathematics with practical use of computer programming, numerical methods and engineering applications. As such, the goal is twofold, first to introduce the more theory oriented students to the beautiful world of engineering applications in control theory, a perfect example of the use of the higher level mathematics in the `real world’, and second, to give a sound theoretical footing to those students that are more applied and engineering oriented.

In parallel, I am developing a Cloud based interactive Controls and Simulation Toolbox that can be accessed at:

Cloud Control Toolbox (CCST)

Warning: These notes are a work in progress.

References:

Optimal Control and Estimation, Robert F. Stengel

Calculus of Variations and Optimal Control Theory, Daniel Liberzon

Optimal Control Theory for Applications, David G. Hull

Applied Optimal Control, Arthur E. Bryson, Jr and Yu-Chi Ho

"An optimal guidance law for planetary landing", Christopher D'Souza, paper, Guidance, Navigation, and Control Conference

Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard, and Dieter Fox

A Course in Robust Control Theory: a convex approach, Geir E. Dullerud and Fernando G. Paganini

Robust and Adaptive Control with Aerospace Applications, Eugene Lavretsky and Kevin A. Wise

Теория автоматического управления, Гольдфарб Л. С, Балтрушевич А. В., Нетушил А. В. и др.

Model References:

2D Car model - I found the original system without the derivation in "Optimal Control and Estimation" by Robert F. Stengel. After not finding a satisfying derivation of the equations in other references I derived it myself as shown in the first section below. Interestingly, the right-hand side of the rate of orientation change turned out to be different from the original model in Stengel.

Landing Spacecraft - Christopher D'Souza. "An optimal guidance law for planetary landing", Guidance, Navigation, and Control Conference, http://dx.doi.org/10.2514/6.1997-3709. Thanks to Ronald Sostaric, NASA, for the suggestion of this paper, which is also used for the Module 2 project.

Table of Context

Module 1 - Classical Control

Introduction and 2D Car Model

Frequency Domain Analysis

Controller Design Basics

Root Locus Analysis

Our First Controller

Extra: Simulating the World

Project 1 - Adaptive Cruise Control

Module 2 - Optimal Control

Introduction

Calculus of Variations

Optimal Control using Calculus of Variations

Maximum Principle

Hamilton-Jacobi-Bellman Equation

The Linear Quadratic Regulator

Project 2 - Planetary Landing Guidance

Module 3 - Optimal State Estimation

Introduction

Simple Estimator

Gaussian Filters

Particle Filters

Extra: Adaptive Filtering

Module 4 - Advanced Topics, Robust and Adaptive Control

Controllability

Eigenvalue Assignment

Observability

H2 Optimal Control

Model Reference Adaptive Control

Robust Adaptive Control