Abstract
This is a case study of application of Global Optimisation (GO), in which a discrete optimal self-tuning controller is designed to regulate the speed of rotor and the amplitude of the rotor flux in an induction motor drive system. Firstly the non-linear dynamics of the induction motor is approximated by a linear model, around its operation point, through a recursive least-squares algorithm. Then the errors between the outputs of the identified linear model and actual rotor are used to train a back-propagation neural network for determining the future control output. With this type of structure, the two control goals of regulating rotor speed and rotor flux amplitude are de-coupled in a nature so that power efficiency can be optimised without affecting speed regulation. A simulated case study is presented to demonstrate the effectiveness of the proposed approach.
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Zhu, Q.M., Guo, L.Z., Ma, Z. (2006). Neural Network Enhanced Optimal Self-tuning Controller Design for Induction Motors. In: Pintér, J.D. (eds) Global Optimization. Nonconvex Optimization and Its Applications, vol 85. Springer, Boston, MA . https://doi.org/10.1007/0-387-30927-6_22
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DOI: https://doi.org/10.1007/0-387-30927-6_22
Publisher Name: Springer, Boston, MA
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