The development of computational power is constantly on the rise and makes for new possibilities in a lot of areas. Two of the areas that has made great progress thanks to this development are control theory and artificial intelligence. The most eminent area of artificial intelligence is machine learning. The difference between an environment controlled by control theory and an environment controlled by machine learning is that the machine learning model will adapt in order to achieve a goal while the classic model needs preset parameters. This supposedly makes the machine learning model more optimal for an environment which changes over time. This theory is tested in this paper on a model of an inverted pendulum. Three different machine learning algorithms are compared to a classic model based on control theory. Changes are made to the model and the adaptability of the machine learning algorithms are tested. As a result one of the algorithms were able to mimic the classic model but with different accuracy. When changes were made to the environments the result showed that only one of the algorithms were able to adapt and achieve balance.
Using machine learning for control systems in transforming environments
2020-01-01
Theses
Electronic Resource
English
DDC: | 629 |
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