In this paper, an adaptive fuzzy control scheme is introduced for permanent magnet synchronous machines (PMSMs). The adaptive control strategy consists of a Lyapunov stability-based fuzzy speed controller that capitalizes on the machine's inverse model to achieve accurate tracking with unknown nonlinear system dynamics. As such, robustness to modeling and parametric uncertainties is achieved. Moreover, no explicit currents loop regulation is needed, which simplifies the control structure and unlike other control strategies, no a priori offline training, weights initialization, parameters knowledge, voltage, or current transducer is required. The system's convergence and stability are proved by Lyapunov stability theory, which yields an improved performance. Simulation results for different situations highlight the performance of the proposed controller in transient, steady-state, and standstill conditions. Furthermore, the adaptive fuzzy systems inherent parallelism makes them a good candidate for implementation in real-time PMSM drive systems.

Additional Metadata
Keywords Artificial intelligence, fuzzy logic, Lyapunov stability, neurofuzzy control, speed control, synchronous machine
Persistent URL dx.doi.org/10.1109/TIE.2011.2148678
Journal IEEE Transactions on Industrial Electronics
Citation
Chaoui, H, & Sicard, P. (Pierre). (2012). Adaptive fuzzy logic control of permanent magnet synchronous machines with nonlinear friction. IEEE Transactions on Industrial Electronics, 59(2), 1123–1133. doi:10.1109/TIE.2011.2148678