How Neural Networks Assist in Motor Control

AI in Embedded Systems How Neural Networks Assist in Motor Control

2026-07-03 A guest post by Jörg Klenke * | Translated by AI 6 min Reading Time

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Modern embedded controllers are becoming increasingly powerful, thus providing realistic possibilities for the use of artificial intelligence in motor control for the first time. At the same time, classical control methods like PID are well-established and industrially proven. So where is the added value of using AI?

Can AI replace classical motor control? Burger Engineering and TH Nürnberg have investigated this.(Image: Burger Engineering / istockphoto.com) — Can AI replace classical motor control? Burger Engineering and TH Nürnberg have investigated this.
(Image: Burger Engineering / istockphoto.com)

Deploy artificial intelligence (AI) directly on embedded systems? This question is increasingly coming into focus for many developers, as today's microcontrollers offer significantly higher computing power than just a few years ago. Additionally, specialized Neural Processing Units (NPUs) are available, enabling the execution of neural networks even under strict resource constraints. Particularly in control engineering, methods such as Reinforcement Learning (RL) and Tiny Machine Learning (TinyML) promise greater robustness, better adaptability to nonlinearities, and potential energy savings.

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