This article presents a pseudospectral convex optimization-based model predictive static programming (PCMPSP) for the constrained guidance problem. First, the sensitivity relation between the state increment and control correction is reformulated using Legendre–Gauss and Legendre–Gauss–Radau pseudospectral transcriptions. Second, the convex optimal control problem associated with the trajectory optimization is defined by introducing the quadratic performance index. Third, modifications to the initial guess solution and reference trajectory update are introduced to enhance the accuracy and robustness of the algorithm. Finally, a model predictive guidance law is designed based on the proposed PCMPSP algorithm for the air-to-surface missile guidance with impact angle constraint. The simulation results show that the PCMPSP has lower sensitivity to the initial guess trajectory, higher accuracy, as well as faster convergence speed than existing convex programming methods. Moreover, the robustness of the proposed guidance law to uncertainties is demonstrated through the Monte Carlo campaign.
Pseudospectral Convex Optimization Based Model Predictive Static Programming for Constrained Guidance
IEEE Transactions on Aerospace and Electronic Systems ; 59 , 3 ; 2232-2244
01.06.2023
3101969 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Autonomous Descent Guidance via Sequential Pseudospectral Convex Programming
Springer Verlag | 2023
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