Abstract
Adaptive dynamic programming (ADP) is a sub-field of approximate dynamic programming that deals with the adaptive control of continuous nonlinear dynamic systems. Its origins stem from dynamic programming in optimal control, but it is extended into a form where approximations are used to reduce the curse of dimensionality and reduce the need for model knowledge. ADP is also considered to be one of the main reinforcement learning (RL) approaches since it uses information obtained from interaction with the environment to improve its policy. RL in general and ADP in particular are well suited for application to autonomous aerospace systems, since they allow adaptive control in case of uncertainties or faults in the system, even if the fault is of a type that is not anticipated during the control design. This chapter first gives a brief historical overview of ADP applications to flight control tasks. After that, four recent advances of ADP for flight control are presented.
| Original language | English |
|---|---|
| Title of host publication | Advances in Industrial Control |
| Editors | Andrea L'Afflitto, Gokhan Inalhan, Hyo-Sang Shin |
| Publisher | Springer |
| Pages | 269-292 |
| Number of pages | 24 |
| ISBN (Electronic) | 978-3-031-39767-7 |
| ISBN (Print) | 978-3-031-39766-0, 978-3-031-39769-1 |
| DOIs | |
| Publication status | Published - 2023 |
Publication series
| Name | Advances in Industrial Control |
|---|---|
| Volume | Part F1768 |
| ISSN (Print) | 1430-9491 |
| ISSN (Electronic) | 2193-1577 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.