Control Engineering with Python

📚 Lecture Slides

Chapter	#	Slide Deck (HTML)
Introduction	0.1	Introduction
Dynamical Systems	1.1	Models
	1.2	Simulation
	1.3	Well-Posedness
	1.4	Asymptotic Behavior
Linear Time-Invariant Systems	2.1	LTI Models
	2.2	Internal Dynamics
	2.3	I/O Behavior
Controller Design	3.1	Controllability
	3.2	Stabilization
	3.3	Optimal Control
Observers	4.1	Observers

Jupyter Notebooks

The course materials are also available as Jupyter notebooks. To view, execute and edit them:

1. Install the pixi package manager.

2. Download and extract this zip archive.¹

3. Inside the project directory, execute the command pixi run start.

Alternatively, if want to use the conda package manager instead of pixi, skip step 1., perform step 2. as above, and then inside the project directory, type conda env create -f environment.yml to create the conda project environment (once and for all). Then whenever you want to browse the notebooks, type conda activate control-engineering-with-python and then jupyter lab.

📚 Open Resources

• 📖 Nicolas Rougier – Scientific Visualization: Python + Matplotlib

• 📖 Eduardo Sontag – Mathematical Control Theory

• 📖 Vladimír Kučera – Riccati Equations and their Solution

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Footnotes

1. or alternatively, if you are a git user, git clone the gh-pages branch of the current repository. ↩