On the role of machine learning in knowledge-based control
Knowledge-based methods gain increasing importance in automation systems. But many real applications are too complex or there is too little understanding to acquire useful knowledge. Therefore machine learning techniques like the directed self-learning which is used here may help to bridge this gap. In order to point out the advantages of machine learning in process automation, we applied the directed self-learning method to the control of an inverted pendulum. Through a comparison between a knowledge-based and a machine learning version of the controller, both based on the knowledge of the same expert, results were achieved which demonstrate the usefulness of machine learning in control applications.
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