Modified High-Order SVD for Spatiotemporal Modeling of Distributed Parameter Systems

Modeling high-spatial dimensional (high-D) distributed parameter systems (DPSs) is very difficult because of the spatially distributed characteristic and complex spatiotemporal coupling. In this paper, a new framework based on high-order singular vector decomposition (HOSVD) is proposed to model high-D DPS. A modified HOSVD is designed for separation of time and multiple spatial variables. It can well preserve the spatially distributed nature of high-D DPS. It also considers the interaction across different spatial modes, which is an important part of spatiotemporal coupling in high-D DPS. Experiments of a two-spatial dimensional thermal curing process are used to verify the effectiveness of the proposed method. An average prediction error near 1% of the curing temperature can be achieved.