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• W3205884071 abstract "Differential Dynamic Programming (DDP) has become a well established method for unconstrained trajectory optimization. Despite its several applications in robotics and controls, however, a widely successful constrained version of the algorithm has yet to be developed. This paper builds upon penalty methods and active-set approaches towards designing a Dynamic Programming-based methodology for constrained optimal control. Regarding the former, our derivation employs a constrained version of Bellman&#x2019;s principle of optimality, by introducing a set of auxiliary slack variables in the backward pass. In parallel, we show how Augmented Lagrangian methods can be naturally incorporated within DDP, by utilizing a particular set of penalty-Lagrangian functions that preserve second-order differentiability. We demonstrate experimentally that our extensions (individually and combinations thereof) enhance significantly the convergence properties of the algorithm, and outperform previous approaches on a large number of simulated scenarios." @default.
• W3205884071 created "2021-10-25" @default.
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• W3205884071 date "2021-05-30" @default.
• W3205884071 modified "2023-10-10" @default.
• W3205884071 title "Constrained Differential Dynamic Programming Revisited" @default.
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• W3205884071 doi "https://doi.org/10.1109/icra48506.2021.9561530" @default.
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