Nothing to see here; I don’t do optimal control. But here are some notes for when I thought I might.
Feedback Systems: An Introduction for Scientists and Engineers by Karl J. Åström and Richard M. Murray is an interesting control systems theory course from Caltech.
Nuts and bolts
Åström et al maintain a supporting python toolkit, python-control.
OPENMODELICA is an open-source Modelica-based modeling and simulation environment intended for industrial and academic usage. Its long-term development is supported by a non-profit organization – the Open Source Modelica Consortium (OSMC).
openMDAO is an open-source high-performance computing platform for systems analysis and multidisciplinary optimization, written in Python. It enables you to decompose your models, making them easier to build and maintain, while still solving them in a tightly coupled manner with efficient parallel numerical methods.
The OpenMDAO project is primarily focused on supporting gradient-based optimization with analytic derivatives to allow you to explore large design spaces with hundreds or thousands of design variables, but the framework also has a number of parallel computing features that can work with gradient-free optimization, mixed-integer nonlinear programming, and traditional design space exploration.