Abstract
A two-loop cascade control system of a DC drive is considered in this research. The task is to keep transients quality in both speed and armature current loops. It is solved by a usage of P- and PI-controller parameters neural tuner, which operates in real time and does not require a plant model. The tuner is trained online during its functioning in order to follow the plant parameters change, but usage of too high values of a learning rate may result in instability of the control system. So, the upper bound of the learning rate value calculation method is proposed. It is based on Lyapunov’s second method application to estimate the system sustainability. It is applied to implement adaptive control of a mathematical model of a two-high rolling mill. Obtained results show that the proposed method is reliable. The tuner allowed to reduce the plant energy consumption by 1–2% comparing to conventional P-controller.
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Acknowledgments
This work was supported by the Russian Foundation for Basic Research. Grant No 15-07-06092.
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Glushchenko, A.I. (2018). Method of Calculation of Upper Bound of Learning Rate for Neural Tuner to Control DC Drive. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research. NEUROINFORMATICS 2017. Studies in Computational Intelligence, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-66604-4_16
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DOI: https://doi.org/10.1007/978-3-319-66604-4_16
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