A combined machine learning and genetic algorithm approach to controller design
The paper describes a combination of machine learning and evolutionary optimisation in learning to control a physical device. Human control skill is exploited as a source of the prior knowledge needed to establish the structure of control rules. This knowledge is first made explicit by means of machine learning, and then a genetic algorithm is used to improve the control performance with respect to chosen criteria. This hybrid control-synthesis scheme was tested in swing control on a model of a container crane. It has proved successful in reducing the number of trials needed for optimisation, which is especially important when dealing with physical devices.
genetic algorithms evolutionary optimisation human control skills dynamic systems control control synthesis inductive machine learning parameter tuning