Deterministic predictive models for DC voltage reference source control

Abstract

In this paper, we use predictive models for voltage correction in a high-precision DC voltage reference source (DCVRS) based on Zener diodes. Voltage reference elements, which compose the DCVRS, are improved by implementing a control loop with built-in predictors. Thus, the sensitivity of the system is reduced and, thereby, the stability of the DCVRS is improved. The predictive abilities of two different paradigms, neural-network-based predictors and difference equation predictors obtained by equation discovery system LAGRAMGE, are compared.