Sparse Bayesian Learning-Based Interval Type-2 Fuzzy Logic Control for Electrospinning Processes

Hongwei Sun, Hai-Tao Zhang, Ning Xing, Yasen Wang, Yang Shi

Abstract: This article develops a closed-loop electrospinning process control system composed of a high-speed industrial camera, an interval type-2 (IT2) fuzzy logic controller (FLC) and a high-precision programmable micropump. A pure data-driven IT2 T-S fuzzy model with a micropump flow input and a fiber diameter output is established by a sparse Bayesian learning (SBL) method, and the closed-loop IT2 FLC is thereby proposed to finely tune the electrospinning fiber diameter according to the technical requirement of the circuit electrospinning process suffered by external disturbances and system uncertainties. Sufficient conditions are derived to guarantee the asymptotical stability of the closed-loop system with the assistance of Lyapunov theory. Experiments on bead-chain structure electrospinning process are conducted to show the effectiveness and superiority of the present SBL-based fuzzy controller.