公开(公告)号：US20250052251A1

公开(公告)日：2025-02-13

申请号：US18884715

申请日：2024-09-13

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Ximing SUN ,  Chongyi SUN ,  Fuxiang QUAN ,  Wei REN ,  Hongxin LI ,  Zhibo ZHANG ,  Mingsui YANG

IPC: F04D27/02 ,  G05D7/06

Abstract: The present invention belongs to the field of aviation compressor control, and relates to an aviation compressor active stabilization control method based on disturbance observation and compensation. Modeling errors and external disturbances of models used in design of a controller are observed, and sub-controllers are individually designed for state variables of interest to compensate for the disturbances, thus to simultaneously solve the problems of rotating stall and surge of an aviation compressor in a variety of complex situations. Partial differential model of the compressor is converted to an ordinary differential equation by Galerkin projection method, partial differential characteristics of the compressor are reserved in the form of disturbances during conversion, and an active stabilization controller of the aviation compressor is designed in combination with disturbance observation and compensation technology, thus to ensure that the models used in the design of the controller have higher accuracy, high robustness and high reliability.

公开(公告)号：US20250045347A1

公开(公告)日：2025-02-06

申请号：US18713854

申请日：2023-08-17

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Ximing SUN ,  Aina WANG ,  Pan QIN ,  Hongxin LI

IPC: G06F17/13

Abstract: The present invention provides a spatiotemporal dynamic system soft sensing method for automatically determining a partial differential equation (PDE) structure and belongs to the technical field of soft sensing of neural networks. Firstly, a loss function for training a coupled physics-informed neural network with a recurrent prediction mechanism is constructed to obtain a solution and a driving source which satisfy a PDE used for describing spatiotemporal industrial processes; secondly, differential operator candidates are obtained by an automatic differentiation method, and an appropriate PDE structure is selected from the differential operator candidates to accurately describe the spatiotemporal industrial processes; and finally, the soft sensing result is verified using heat diffuse phenomena and actual vibration processes. The CPINNRP-AIC is suitable for soft sensing methods of multi-class dynamic systems with spatiotemporal dependence, can achieve the effective acquisition of key variable values for high-end complex equipment such as an aero-engine in operation processes.