  The growing demand for accurate control in varying and unknown environments
has sparked a corresponding increase in the requirements for power supply
components, including permanent magnet synchronous motors (PMSMs). To infer the
unknown part of the system, machine learning techniques are widely employed,
especially Gaussian process regression (GPR) due to its flexibility of
continuous system modeling and its guaranteed performance. For practical
implementation, distributed GPR is adopted to alleviate the high computational
complexity. However, the study of distributed GPR from a control perspective
remains an open problem. In this paper, a control-aware optimal aggregation
strategy of distributed GPR for PMSMs is proposed based on the Lyapunov
stability theory. This strategy exclusively leverages the posterior mean,
thereby obviating the need for computationally intensive calculations
associated with posterior variance in alternative approaches. Moreover, the
straightforward calculation process of our proposed strategy lends itself to
seamless implementation in high-frequency PMSM control. The effectiveness of
the proposed strategy is demonstrated in the simulations.
