xLSTM⁃Based Excitation Current Prediction for Synchronous Machines Towards Electro⁃spindle DrivesOA

Accurate excitation current prediction is crucial for the high-performance control of synchronous machines(SMs),which are widely employed in industrial drives such as electro-spindles.However,achieving accurate and generalizable prediction across multiple operating points is challenging due to coupled nonlinearities like thermal drift and magnetic saturation.This study proposes a novel prediction model based on the extended long short-term memory(xLSTM)network.The model integrates scalar LSTM(sLSTM)and matrix LSTM(mLSTM)units and leverages an exponential gating mechanism to enhance the capability for learning complex nonlinear mappings and long-term dependencies.Specifically,the vectorized parallel memory structure of sLSTM is suited to capturing slow parameter variations caused by thermal drift,while the matrix associative memory mechanism of mLSTM excels at learning multi-variable nonlinear coupling effects such as magnetic saturation.These two modules form a complementary hybrid architecture.Comparative analyses against traditional LSTM and gate recurrent unit(GRU)benchmarks were conducted using SM monitoring data covering various load and excitation conditions.In addition,an ablation study was performed using xLSTM with varying blending ratios of scalar and matrix LSTM components.Evaluation based on multiple error metrics and computational time demonstrates that the proposed xLSTM achieves superior accuracy,stronger generalization,lower computational overhead,and higher prediction stability.The underlying mechanisms are analyzed from architectural and algorithmic perspectives.These findings offer a novel datadriven modeling approach for SM excitation current,with potential value for applications requiring high-fidelity motor state estimation.