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 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.

List of contents

Introduction.- Problem Statement.- Asymptotic Maximum Likelihood Identification.- Geometric Results.- Lagrangian Adaptation.- Proof of Theorem 5.2.- Index.

Product details

• Authors: Peter E. Caines, David Levanony
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 09.11.2024
• EAN: 9783031737572
• ISBN: 978-3-0-3173757-2
• No. of pages: 77
• Dimensions: 155 mm x 4 mm x 235 mm
• Weight: 143 g
• Illustrations: VI, 77 p.
• Series: SpringerBriefs in Mathematics
• Subject: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics