An ANN controller adapted for very high frequency responses

What is it about?

In this paper, a self-tuning algorithm for proportional integral derivative (PID) control based on the adaptive interaction (AI) approach theory efficiently used in artificial neural networks (ANNs) is proposed. In this approach, a system is decomposed into interconnected subsystems, and adaptation occurs in the interaction weights among these subsystems. The principle behind the adaptation algorithm is mathematically equivalent to a gradient descent algorithm. The same adaptation as the well-known back-propagation algorithm (BPA) can be achieved without the need of a feedback network, which would propagate the errors, by applying adaptive interaction.

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Why is it important?

(i) The PID tuning algorithm proposed in this paper has many advantages like a low error rate, low calculation time, low memory usage,and high frequency response in BLDCM control applications. (ii) The simulation results shows that it performs very well even in constantly changing load conditions. (iii) This new approach does not require the transformation of the continuous time domain plant into its NN equivalent. Another benefit of applying the proposed algorithm is that it does not require a separate feedback network to back-propagate the error.