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.

翻译：在多变和未知环境下精确控制的需求日益增长，这相应提升了对包括永磁同步电机（PMSM）在内的电源组件的要求。为推断系统中的未知部分，机器学习技术被广泛采用，尤其是高斯过程回归（GPR），因其连续系统建模的灵活性和性能保障而备受青睐。在实际应用中，分布式高斯过程回归被用于缓解高计算复杂度问题。然而，从控制视角研究分布式高斯过程回归仍是一个开放性问题。本文基于李雅普诺夫稳定性理论，提出了一种面向控制的永磁同步电机分布式高斯过程最优聚合策略。该策略仅利用后验均值，从而避免了其他方法中与后验方差相关的计算密集型运算。此外，所提策略简洁的计算过程使其适用于高频永磁同步电机控制的无缝实现。仿真结果验证了所提策略的有效性。