Modeling, Dynamical Analysis, and Neural Funnel Synchronization Control of Fractional-Order Coupled PMSMs

Industrial internet of things (IoT) is an intelligent system to realize data sharing and interconnectivity among devices by wired or wireless networks wherein the synchronization compatibility is essential. This article investigates the modeling, dynamical analysis and neural funnel synchronization control of fractional-order (FO) coupled permanent magnet synchronous motors (PMSMs) for industrial IoT. Considering intrinsic FO characteristics and resistive coupling, the FO synchronization model between primary and secondary PMSMs are first built. Second, dynamical analysis reveals that it generates rich dynamical behaviors like periodic motion, transient/chaotic oscillation during the synchronization process. The designed hardware circuit further confirms chaotic oscillation which significantly influences system stability. The problem is rather complicated but still challenging if uncertainty, performance constraint, communication limit and synchronization are all addressed and effectively resolved within a short time. Then a neural funnel synchronization control scheme is proposed by designing a funnel boundary function, introducing a hyperbolic tangent tracking differentiator (HTTD) and constructing an event-triggered mechanism under the hierarchical neural network (HNN). We construct an experimental testbed primarily consisting of driver boards, primary/secondary PMSM, encoders and control boards, and write driven codes for actual implementation including initialization, interruption, control and protection. Finally, experimental results further testify the feasibility of our scheme.