Abstract

Hybrid rocket motors are an attractive option for the upper-stages of low-cost small launchers, but are susceptible to variability in performance both in time and between firings. Moreover, key contributors to hybrid motors’ performance such as oxidizer-to-fuel ratio (O/F) are difficult to estimate, and by extension, to control. Four approaches were evaluated for the estimation and control of O/F under system uncertainty, including through on-line estimation by an Unscented Kalman Filter (UKF). A Monte Carlo analysis was conducted of a simulated hybrid kick motor, where key sources of system uncertainty such as the characteristic velocity efficiency (ηc*), fuel regression coefficients, and oxidizer flow characteristics were allowed to be variable. Feedback control of O/F informed by the UKF obtained 6.8% smaller control error than the best alternative approach. Yet the Monte Carlo analysis showed that among uncertainty sources considered, ηc* was the primary driver of performance variability, while O/F regulation had a small influence. This was because the total and specific impulses were relatively insensitive to O/F for the considered motor configuration and ranges of O/F observed during the simulated burns—highlighting the importance of system uncertainty quantification when formulating performance-regulating interventions. Further, the proposed UKF observer provided data-informed estimates of combustion efficiency and propellant residuals in time, which are valuable for the planning and execution of accurate orbital insertions in a kick motor susceptible to performance uncertainty. The developed uncertainty quantification and control modeling framework can be used also during the design and assessment of other control interventions under system uncertainty.

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