Machine learning control (MLC) is a subfield of machine learning, intelligent control, and control theory which aims to solve optimal control problems with machine learning methods.
Key applications are complex nonlinear systems for which linear control theory methods are not applicable.
Four types of problems are commonly encountered: MLC has been successfully applied to many nonlinear control problems, exploring unknown and often unexpected actuation mechanisms.
Example applications include: Many more engineering MLC application are summarized in the review article of PJ Fleming & RC Purshouse (2002).
[11] As is the case for all general nonlinear methods, MLC does not guarantee convergence, optimality, or robustness for a range of operating conditions.