Sensitivity-based state and parameter moving horizon estimation method for liquid propellant rocket engine

The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation. State and parameter Moving Horizon Estimation (MHE) is an optimization-based strategy that provides the necessary information for model predictive control. Despite the many advantages of MHE, long computation time has limited its applications for system-level models of liquid propellant rocket engines. To address this issue, we propose an asynchronous MHE method called advanced-multi-step MHE with Noise Covariance Estimation (amsMHE-NCE). This method computes the MHE problem asynchronously to obtain the states and parameters and can be applied to multi-threaded computations. In the background, the state and covariance estimation optimization problems are computed using multiple sampling times. In real-time, sensitivity is used to quickly approximate state and parameter estimates. A covariance estimation method is developed using sensitivity to avoid redundant MHE problem calculations in case of sensor degradation during engine reuse. The amsMHE-NCE is validated through three cases based on the space shuttle main engine system-level model, and we demonstrate that it can provide more accurate real-time estimates of states and parameters compared to other commonly used estimation methods.