Abstract
Control systems are in general based on the same structure, building blocks and physics-based models of the dynamic system regardless of application, and can be mathematically analyzed w.r.t. stability, robustness and so on given certain assumptions. Machine learning methods (ML), on the other hand, are highly flexible and adaptable methods but are not subject to physic-based models and therefore lack mathematical analysis. This paper presents state of the art results using ML in the control system. Furthermore, a case study is presented where a neural network is trained to mimic a feedback linearizing speed controller for an autonomous ship. The neural network outperforms the traditional controller in case of modeling errors and measurement noise.
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Notes
- 1.
A narrated video summary is available at http://bit.ly/perceivingtrails/.
- 2.
A video summary is available at http://rll.berkeley.edu/icra2016mpcgps/.
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Acknowledgments
This project has been supported through the basic funding from the Norwegian Research Council.
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Moe, S., Rustad, A.M., Hanssen, K.G. (2018). Machine Learning in Control Systems: An Overview of the State of the Art. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science(), vol 11311. Springer, Cham. https://doi.org/10.1007/978-3-030-04191-5_23
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