Reduced-Order Modeling and Convex Model Predictive Control for Rocket Engine Thrust Regulation

Abstract

Accurate and safe thrust control of rocket engines requires regulating the combustion chamber pressure and oxidizer-to-fuel ratio within strict operational limits. This paper presents a convex successive linear Model Predictive Control (SLMPC) framework for setpoint tracking of these variables in a pressure-fed rocket engine under actuator and state constraints. A lumped parameter dynamic model is derived and subsequently reduced by neglecting fast fluid dynamics. This yields a two-state representation that preserves the dominant engine dynamics while offering reduced computational complexity when employed for MPC. The resulting SLMPC formulation achieves accurate pressure and mixture-ratio control. Simulation results demonstrate that the proposed approach outperforms a conventional PI controller in tracking speed and constraint satisfaction, while closely matching the performance of a nonlinear MPC. The SLMPC requires 6 ms per solve at 30 Hz, which is six times faster than the nonlinear MPC.