This paper presents an iterative decimation approach to significantly alleviate the computational burden of centralized controllers applying real-time recursive system identification algorithms in multi-rail power converters. The proposed approach uses an adaptive update rate as opposed to the fixed update rate used in conventional adaptive filters. Also, the step size/forgetting factors vary at different iteration stages. As a result, a reduced computational burden and faster model update can be achieved. Besides, recursive algorithms, such as Recursive Least Square (RLS), Fast Affine Projection (FAP) and Kalman Filter (KF), contain two important updates per iteration cycle; Covariance Matrix Approximation (CMA) update and Gradient Vector (GV) update. Usually, the CMA update requires the greater computational effort than the GV update. Therefore, in circumstances where the sampled data in the regressor does not experience significant fluctuations, re-using the CMA, calculated from the last iteration cycle for the current update can result in computational cost savings for real-time system identification. In this paper, both iteration rate adjustment and CMA re-cycling are combined and applied to simultaneously identify the power converter models in a three-rail power conversion architecture.

中文翻译：

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使用迭代抽取方法的多轨电源转换器系统的集中系统识别

本文提出了一种迭代抽取方法，以显着减轻在多轨电源转换器中应用实时递归系统识别算法的集中控制器的计算负担。所提出的方法使用自适应更新率，而不是传统自适应滤波器中使用的固定更新率。此外，步长/遗忘因子在不同的迭代阶段有所不同。因此，可以实现减少的计算负担和更快的模型更新。此外，递归算法，如递归最小二乘 (RLS)、快速仿射投影 (FAP) 和卡尔曼滤波器 (KF)，每个迭代周期包含两个重要更新；协方差矩阵逼近 (CMA) 更新和梯度向量 (GV) 更新。通常，CMA 更新需要比 GV 更新更大的计算量。所以，在回归器中的采样数据没有经历显着波动的情况下，重新使用从当前更新的最后一个迭代周期计算的 CMA 可以节省实时系统识别的计算成本。在本文中，迭代率调整和 CMA 循环结合并应用于同时识别三轨电源转换架构中的电源转换器模型。