Abstract
In this work we will report our initial investigation of how a neural network architecture could become an efficient tool to model Proportional-Integral-Derivative controller (PID controller). It is well known that neural networks are excellent function approximators, we will then be investigating if a recursive neural networks could be suitable to model and tune PID controllers thus could assist in determining the controller’s proportional, integral, and the derivative gains. A preliminary evaluation is reported.
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Marino, A., Neri, F. (2019). PID Tuning with Neural Networks. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11431. Springer, Cham. https://doi.org/10.1007/978-3-030-14799-0_41
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