A combined machine learning and genetic algorithm approach to controller design

Abstract 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.

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