Linear-Quadratic Controls in Risk-Averse Decision Making

Performance-Measure Statistics and Control Decision Optimization

  • Khanh D. Pham
Part of the SpringerBriefs in Optimization book series (BRIEFSOPTI)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Khanh D. Pham
    Pages 1-5
  3. Khanh D. Pham
    Pages 145-147
  4. Back Matter
    Pages 149-150

About this book

Introduction

​​Linear-Quadratic Controls in Risk-Averse Decision Making   cuts across control engineering (control feedback and decision optimization) and statistics (post-design performance analysis) with a common theme: reliability increase seen from the responsive angle of incorporating and engineering multi-level performance robustness beyond the long-run average performance into control feedback design and decision making and complex dynamic systems from the start. This monograph provides a complete description of statistical optimal control (also known as cost-cumulant control) theory. In control problems and topics, emphasis is primarily placed on major developments attained and explicit connections between mathematical statistics of performance appraisals and decision and control optimization. Chapter summaries shed light on the relevance of developed results, which makes this monograph suitable for graduate-level lectures in applied mathematics and electrical engineering with systems-theoretic concentration, elective study or a reference for interested readers, researchers, and graduate students who are interested in theoretical constructs and design principles for stochastic controlled systems.​ 

Keywords

Mayer problem chi-squared random cost cumulant-generating function performance-measure statistics risk-averse control feedback stochastic linear systems

Authors and affiliations

  • Khanh D. Pham
    • 1
  1. 1., Space Vehicles DirectorateThe Air Force Research LaboratoryKirtland Air Force BaseUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-5079-5
  • Copyright Information Khanh D. Pham 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-5078-8
  • Online ISBN 978-1-4614-5079-5
  • Series Print ISSN 2190-8354
  • Series Online ISSN 2191-575X
  • About this book