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A skip entry guidance algorithm blending numerical predictor-corrector and nominal trajectory tracking is presented for lunar return vehicles.The guidance is decoupled into longitudinal and lateral channels.A piecewise bank-vs-energy magnitude profile and a sign profile are adopted in the skip phase.A magnitude parameter is used to adjust the predicted downrange,and a pseudo-crossrange at the beginning of the final phase is selected as the lateral control variable.Prediction biases of both channels are nullified by a false position iteration algorithm.An on-line estimation and modeling method is introduced to compensate for aerodynamic and atmospheric uncertainties.A nominal trajectory for the final phase is generated based on actual reenter conditions,and the obtained nominal trajectory is tracked by a linear feedback law.A lateral corridor is used to manage the lateral state.The proposed guidance algorithm is assessed using three-degree-of-freedom Monte Carlo analyses,and the results show a satisfactory and robust performance under highly stressful dispersions.
A skip entry guidance algorithm convergence numerical predictor-corrector and nominal trajectory tracking is presented for lunar return vehicles. The guidance is decoupled into longitudinal and lateral channels. A piecewise bank-vs-energy magnitude profile and a sign profile are in the skip phase . A magnitude parameter is used to adjust the predicted downrange, and a pseudo-crossrange at the beginning of the final phase is selected as the lateral control variable. Predictive biases of both channels are nullified by a false position iteration algorithm. Ann on-line estimation and modeling method is introduced to compensate for aerodynamic and atmospheric uncertainties. A nominal trajectory for the final phase is generated based on actual reenter conditions, and the obtained nominal trajectory is tracked by a linear feedback law. A lateral corridor is used to manage the lateral state. suggests a guidance algorithm is assessed using three-degree-of-freedom Monte Carlo analyzes, and the results show a satisfactory and robust performance under highly stressful dispersions.