A flow inferential measurement of the independent metering multi-way valve based on an improved RBF neural network

The flow inferential measurement is a crucial way to conduct the electrohydraulic (EH) flow control of independent metering multi-way valves (IMMV). However, valve flow is nonlinearly and uncertainly affected by multiple parameters, which makes its estimation inaccurate. In this paper, a flow inferential measurement method based on an improved radial basis function neural network (RBFNN) is proposed. A three-input and one-output RBFNN is designed utilizing the Gaussian functions to train the flow mapping in terms of the tested flow data. A particle swarm optimization (PSO) combined with the least squares algorithm is presented to optimize the sensitive and irregular parameters of RBFNN, such as center, width, and weight. Furthermore, a linear time-varying factor (LTVF) strategy is adopted to enhance the global search capability of the particle swarm. Experiments demonstrate that compared with other neural network-based flow calculation methods, the proposed LTVF-PSO-RBF method achieves superior accuracy with improvements of 13.08 %-19.83%.