Fault tolerant convex guidance for lunar powered descent

Date

2025-8

Author

Kuran, Betül Rana

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This thesis focuses on the development and evaluation of a fault-tolerant convex guidance algorithm for the powered descent phase of lunar soft landing missions. The guidance algorithm for powered descent is critical for achieving precise landing accuracy and optimal fuel efficiency. The non-convex optimal control problem for lunar soft landings is successfully transformed into a convex problem using the lossless convexification approach. This transformation ensures computational efficiency and guarantees optimality. The resulting convex guidance algorithm is implemented for onboard application using the Model Predictive Control (MPC) framework. As a result, the guidance algorithm can adapt the trajectory in response to thrust drop faults and system uncertainties during the powered descent. The convex guidance algorithm is tested for various thrust drop scenarios across different magnitudes and timings by using Chang'e-3 data. The results demonstrated that landing accuracy increases as the thrust drop magnitude decreases and as fault occurrences move toward the later moments of the descent phase. Furthermore, minimal landing errors were observed when faults occurred early with low-magnitude thrust drops. This robust and adaptive guidance methodology substantially contributes to ensuring mission safety and enhancing overall spacecraft autonomy.

Subject Keywords

Lossless convexification, Powered descent, Onboard guidance, Lunar soft landing, Model predictive control

URI

https://hdl.handle.net/11511/115590

Collections

Graduate School of Natural and Applied Sciences, Thesis