Stochastic Lagrangian Adaptation
This book introduces a cutting-edge continuous time stochastic linear quadratic (LQ) adaptive control algorithm for fully observed linear stochastic systems with unknown parameters. The adaptive estimation algorithm is engineered to drive the maximum likelihood estimate into the set of parameters re...
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| Main Authors: | , |
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| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | inglés |
| Publicado: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Edición: | 1st ed. 2024. |
| Series: | SpringerBriefs in Mathematics,
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| Subjects: | |
| Acceso en liña: | https://doi.org/10.1007/978-3-031-73758-9 |
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| Summary: | This book introduces a cutting-edge continuous time stochastic linear quadratic (LQ) adaptive control algorithm for fully observed linear stochastic systems with unknown parameters. The adaptive estimation algorithm is engineered to drive the maximum likelihood estimate into the set of parameters representing the true closed-loop dynamics. By incorporating a performance monitoring feature, this approach ensures that the estimate converges to the true system parameters. Concurrently, it delivers optimal long-term LQ closed-loop performance. This groundbreaking work offers a significant advancement in the field of stochastic control systems. |
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| Descrición Física: | 1 online resource (VI, 77 p.). |
| ISBN: | 9783031737589 |
| ISSN: | 2191-8201 |
| DOI: | 10.1007/978-3-031-73758-9 |