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Fourier-Hermite Dynamic Programming for Optimal Control
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DOI：10.1109/TAC.2023.3234236.png)
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Abstract
En 中文
In this article, we propose a novel computational method for solving nonlinear optimal control problems. The method is based on the use of Fourier-Hermite series for approximating the action-value function arising in dynamic programming instead of the conventional Taylor-series expansion used in differential dynamic programming. The coefficients of the Fourier-Hermite series can be numerically computed by using sigma-point methods, which leads to a novel class of sigma-point-based dynamic programming methods. We also prove the quadratic convergence of the method and experimentally test its performance against other methods.
Keywords:
Approximate dynamic programming
differential dynamic programming
Fourier-Hermite series
sigma-point dynamic programming
trajectory optimization
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W