Title: System identification of proportional solenoid valve dynamics

Authors: Bakir Hajdarevic; Jacob Herrmann; Andrea Fonseca Da Cruz; David W. Kaczka

Addresses: Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242 USA; Department of Anesthesia, University of Iowa, Iowa City, IA, 52242 USA ' Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242 USA; Department of Anesthesia, University of Iowa, Iowa City, IA, 52242 USA ' Pulmonary Division, Universidade de Sao Paulo, Sao Paulo, SP, Brazil ' Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242 USA; Department of Anesthesia, University of Iowa, Iowa City, IA, 52242 USA; Department of Radiology, University of Iowa, Iowa City, IA, 52242 USA

Abstract: We present a system identification technique for the characterisation of the linearity and dynamic response of a PSOL valve and its corresponding electronic control unit (ECU) using bandlimited white noise, as well as pseudo random "non-sum non-difference" (NSND) waveforms consisting of mutually prime frequencies to mitigate the effects of nonlinear distortions. The parameters of several transfer function models were simultaneously estimated from the voltage-flow frequency response using a nonlinear gradient descent technique. Candidate transfer function models were assessed using the mean squared residual (MSR) criterion and the corrected Akaike information criterion (AICc). The MSR yielded a transfer function consisting of ten poles and nine zeros, while the AICc yielded a simpler transfer function consisting of five poles and three zeros. Monte Carlo analysis demonstrated fragile stability for the MSR-selected model with respect to varying parameter values within estimated uncertainties, yet a robust stability for the AICc-selected model.

Keywords: PSOL; proportional solenoid valve; MSRs; mean squared residuals; AIC; Akaike information criterion; system identification; transfer functions; model optimisation; pneumatic systems.

DOI: 10.1504/IJMIC.2020.110349

International Journal of Modelling, Identification and Control, 2020 Vol.34 No.2, pp.103 - 115

Received: 24 Jun 2019
Accepted: 06 Jan 2020
Published online: 15 Oct 2020 *

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