Sensorless wind energy conversion system maximum power point tracking using Takagi-Sugeno fuzzy cerebellar model articulation control

Graphical abstractDisplay Omitted HighlightsTS-CMAC achieves sensorless WECS MPPT.Increases accuracy of CMAC initial weights.Introduces adaptive ability in LMI-based design.Relaxes assumption on system uncertainty.We carry out numerical simulations under various wind speeds to show exponential convergence. In this paper, we propose a sensorless wind energy conversion system (WECS) maximum wind power point tracking using Takagi-Sugeno fuzzy cerebellar model articulation control (T-S CMAC). The main objective of the WECS is to achieve maximum power transfer under various wind speeds without actual measurement of the wind velocity. We first represent the WECS, which uses a permanent magnet synchronous generator (PMSG), as a nonlinear dynamical model. To carry out the T-S CMAC design, we rewrite the WECS model as a T-S fuzzy representation. The T-S CMAC design is inspired by the architectural similarity of the T-S fuzzy control and CMAC where accordingly the PDC design control gains and weighting parameter are augmented into a single vector. The advantages of this approach are 3-fold: (i) increases accuracy of CMAC initial weights - we assign the initial weights of CMAC using the control gains solved by the LMIs from the PDC design; (ii) introduces adaptive ability in LMI-based design - the CMAC design allows time-varying parameters in the system; and (iii) relaxes assumption on system uncertainty - we drop the assumption that a strict upper bound on system uncertainty is known. Numerical simulations under various wind speeds show exponential convergence results which further verify the theoretical derivations.