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2024 | OriginalPaper | Chapter

4. An Improved Differential Dynamic Programming Approach for Computational Guidance

Authors : Xiaobo Zheng, Shaoming He, Defu Lin

Published in: Control of Autonomous Aerial Vehicles

Publisher: Springer Nature Switzerland

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Abstract

Differential dynamic programming (DDP) is a well-recognized method for computational guidance due to its fast convergence characteristics. However, the original DDP requires a predefined final time and cannot handle nonlinear constraints in optimization. This prohibits the application of DDP to autonomous vehicles due to the heuristic nature of setting a final time beforehand and the existence of inherent physical limits. This chapter revisits DDP by dynamically optimizing the final time via the first-order optimality condition of the value function and using the augmented Lagrangian method to tackle nonlinear constraints. The resultant algorithm is termed flexible final time-constrained differential dynamic programming (FFT-CDDP). Extensive numerical simulations for a three-dimensional guidance problem are used to demonstrate the working of FFT-CDDP. The results indicate that the proposed FFT-CDDP provides much higher computational efficiency and stronger robustness against the initial solution guess, compared with the commercial-off-the-shelf GPOPS toolbox.

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Metadata
Title
An Improved Differential Dynamic Programming Approach for Computational Guidance
Authors
Xiaobo Zheng
Shaoming He
Defu Lin
Copyright Year
2024
DOI
https://doi.org/10.1007/978-3-031-39767-7_4

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