Low-Complexity Architecture for Cyber-Physical Systems Model Identification
Low-Complexity Architecture for Cyber-Physical Systems Model Identification
复制标题
DOI：
10.1109/tcsii.2018.2881481
复制
发表时间：
2019-08
期刊：
影响因子：
--
通讯作者：
Charan Kumar Vala;M. French;A. Acharyya;B. Al-Hashimi
中科院分区：
文献类型：
--
作者：
Charan Kumar Vala;M. French;A. Acharyya;B. Al-Hashimi
提出了一种基于多模型自适应估计（MMAE）算法的信息物理系统（CPS）模型辨识的低复杂度体系结构。通过减少CPS的网络组件中存在的MMAE算法的滤波器组中的乘法次数来实现复杂度降低。该架构已实现使用FPGA的16，32，64滤波器组作为自主汽车应用的位置和速度估计的一部分。已经发现，在100MHz的工作频率下，与传统架构（没有乘法减少）相比，乘法减少高达78%是可能的，这意味着减少了39%的查找表，13%的FF，27%的DSP和43%的功耗降低。此外，所提出的架构是能够识别准确的汽车移动应用模型仅在510 ns内，在存在外部干扰和突变。
We propose a low complexity architecture for cyber-physical system (CPS) model identification based on multiple-model adaptive estimation (MMAE) algorithms. The complexity reduction is achieved by reducing the number of multiplications in the filter banks of the MMAE algorithm present in the cyber component of the CPS. The architecture has been implemented using FPGA for 16, 32, 64 filter banks as part of position and velocity estimations of autonomous auto-mobile application. It has been found up to 78% reduction in multiplications is possible, which translates to the reduction of 39% lookup tables, 13% FFs, 27% DSPs, and 43% power reduction when compared with the conventional architecture (without multiplications reduction) at 100MHz operating frequency. Furthermore, the proposed architecture is able to identify accurate model of auto-mobile application just within 510 ns, in the presence of external disturbances and abrupt changes.