Introduces a stochastic DDP algorithm that optimizes nominal controls and feedback gains for belief-state trajectory problems under partial observability without relying on the separation principle.
Stochastic Differential Dynamic Programming with Unscented Transform for Low-Thrust Trajectory Design
4 Pith papers cite this work, alongside 69 external citations. Polarity classification is still indexing.
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SODA uses differential algebra and adaptive Gaussian mixtures to solve chance-constrained nonlinear trajectory optimization problems for space missions with non-Gaussian uncertainties.
Two general-purpose methods transcribe multi-dimensional Gaussian chance constraints for trajectory optimization with reduced conservatism, paired with a quadratic-complexity risk estimator that remains accurate in high dimensions.
DADDy combines differential dynamic programming with differential algebra to accelerate constrained fuel-optimal low-thrust trajectory optimization, reporting 41-88% runtime reductions on Sun-centred, Earth-Moon and Earth-centred benchmarks while retaining convergence.
citing papers explorer
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Stochastic Differential Dynamic Programming for Trajectory Optimization under Partial Observability
Introduces a stochastic DDP algorithm that optimizes nominal controls and feedback gains for belief-state trajectory problems under partial observability without relying on the separation principle.
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Non-linear stochastic trajectory optimisation
SODA uses differential algebra and adaptive Gaussian mixtures to solve chance-constrained nonlinear trajectory optimization problems for space missions with non-Gaussian uncertainties.
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Chance constraints transcription and failure risk estimation for stochastic trajectory optimisation
Two general-purpose methods transcribe multi-dimensional Gaussian chance constraints for trajectory optimization with reduced conservatism, paired with a quadratic-complexity risk estimator that remains accurate in high dimensions.
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Taylor polynomial-based constrained solver for fuel-optimal low-thrust trajectory optimisation
DADDy combines differential dynamic programming with differential algebra to accelerate constrained fuel-optimal low-thrust trajectory optimization, reporting 41-88% runtime reductions on Sun-centred, Earth-Moon and Earth-centred benchmarks while retaining convergence.