Abstract
This paper presents the synthesis of an adaptive PID type controller in which the variable-order fractional operators are used. Due to the implementation difficulties of fractional order operators, both with a fixed and variable order, on digital control platforms caused by the requirement of infinite memory resources, the fractional operators that are part of the discussed controller were approximated by recurrent neural networks based on Gated Recurrent Unit cells. The study compares the performance of the proposed neural controller with other solutions, which are based on definitional fractional-order operators exploiting an infinite memory buffer and a classical adaptive PID controller. The proposed neural approximations of variable-order fractional operators applied to a PID-type controller provide a viable solution that can be successfully implemented on present-day digital control platforms. The research presented here focuses on the aspects of accuracy of approximators in simulated operating conditions within the thermal power control system of the challenging plant such as Small Modular Nuclear Reactor.
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Acknowledgements
Financial support of these studies from Gdańsk University of Technology by the DEC-33/2020/IDUB/I.3.3 grant under the ARGENTUM - ‘Excellence Initiative - Research University’ program is gratefully acknowledged.
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Puchalski, B., Rutkowski, T.A., Tarnawski, J., Karla, T. (2023). Recurrent Neural Network Based Adaptive Variable-Order Fractional PID Controller for Small Modular Reactor Thermal Power Control. In: Kowalczuk, Z. (eds) Intelligent and Safe Computer Systems in Control and Diagnostics. DPS 2022. Lecture Notes in Networks and Systems, vol 545. Springer, Cham. https://doi.org/10.1007/978-3-031-16159-9_17
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