PID Tuning with Neural Networks

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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