Dynamics Analysis and Control of Fractional-Order Synchronous Reluctance Motor Based on Chaotic Neurons and ZNN

With the widespread application of motor drive systems in fields such as industrial automation and new energy vehicles, the impact of their nonlinear dynamical behavior on control accuracy and stability has become increasingly significant. Chaos theory provides new insights for revealing and regulating complex nonlinear phenomena in motor systems. Based on chaos theory, this paper takes the synchronous reluctance motor as the research object and proposes, for the first time, a fractional-order mathematical model of the synchronous reluctance motor based on chaotic neurons. Then, the chaotic dynamical behaviors of the fractional-order mathematical model at orders of 0.99 and 0.97 were analyzed. Through bifurcation analysis, Lyapunov exponents, Poincare sections, and attraction domains reveal the mechanism of chaotic oscillation induced by external excitation current and multiple parameter modulation factors. Subsequently, a closed-loop control system based on the Zeroing Neural Network (ZNN) algorithm was designed, which effectively suppressed the chaotic behavior in the motor system’s mechanical rotor angular velocity, phase current, and rotor electrical angular velocity, thereby significantly enhancing the system’s stability. Finally, the effectiveness of the proposed method was validated through simulation experiments, providing theoretical support for the design of motor drive systems.