Abstract: Y. A. Aman, N. C. Sahoo (2026)

Switched reluctance motors (SRMs) are inherently nonlinear systems due to their magnetic saturation, doubly salient structure, and discrete phase commutation. Under certain operating conditions, these nonlinearities can give rise to chaotic dynamics, which may significantly degrade drive performance and, in extreme cases, lead to instability. Understanding the onset and characteristics of chaos in SRM drives is therefore essential for reliable operation. Offline chaos analysis offers a powerful means to investigate such complex dynamics in detail, beyond what is typically feasible in real-time. In this work, a comprehensive and model-free investigation of chaotic behavior in SRM drives is carried out using experimentally measured data. Multiple numerical-based chaos analysis techniques, including the largest Lyapunov exponent, the 0â€“1 test for chaos, entropic chaos degree, Fractal Dimension, and Recurrence plots are implemented to quantify sensitivity to initial conditions and complexity in the system response. In addition, signal processing-based techniques such as the short-time Fourier transform, wavelet transform, and Hilbertâ€“Huang transform are employed to analyze timeâ€“frequency and timeâ€“scale characteristics associated with chaotic operation. These techniques are systematically and comparatively evaluated in terms of sensitivity, robustness, and computational suitability for practical SRM applications. Based on the insights obtained from this comparative study, a real-time chaos monitoring algorithm is developed and, for the first time, successfully deployed on an operating SRM drive. Experimental results demonstrate the practicality of the proposed framework and highlight its potential for early identification and mitigation of undesirable nonlinear and chaotic dynamics in SRM drives.

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Monitoring chaotic behavior in switched reluctance motors: implementing numerical and signal processing based chaos analysis techniques and development of a real-time chaos detection algorithm