The research of multi-dimensional flow mapping for proportional flow control valve based on long short-term memory network

Abstract The multidimensional flow mapping illustrates the numerical correlation between the flow rate and three pertinent factors, namely spool displacement, valve port pressure difference, and oil temperature. This representation signifies a mapping of three-dimensional inputs to a one-dimensional output. Accurate mapping relationship lays solid foundation for the feasible flow control of the flow control proportional valve. Since the proportional flow control valve is a complex system with high nonlinearity, it is challenging to treat the flow mapping relationship as a simple parameter estimation problem. In this regard, this paper combines the relevant theories of signal processing and deep learning (DL) to propose a novel four-dimensional input-output mapping relationship learning method. This method adopts the long short-term memory (LSTM) network to model the multi-dimensional flow mapping relationship. And for better quality of the data, the initial measurement datasets, contaminated by environmental factors, are processed using a finite impulse response (FIR) filter to reduce noise before training the data. Moreover, the trained model is validated on test datasets. The experimental results shows that the mentioned method can accurately estimate the multidimensional mapping relationship of the proportional flow control valve.