Machine learning control (MLC) is a subfield of machine learning, intelligent control and control theory which solves optimal control problems with methods of machine learning. Key applications are complex nonlinear systems for which linear control theory methods are not applicable.
Four types of problems are commonly encountered.
MLC comprises, for instance, neural network control, genetic algorithm based control, genetic programming control, reinforcement learning control, and has methodological overlaps with other data-driven control, like artificial intelligence and robot control.
MLC has been successfully applied to many nonlinear control problems, exploring unknown and often unexpected actuation mechanisms. Example applications include
As for all general nonlinear methods, MLC comes with no guaranteed convergence, optimality or robustness for a range of operating conditions.