Linear-quadratic controls in risk-averse decision making [electronic resource] : performance-measure statistics and control decision optimization /
Pham, Khanh D.author.
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.  .
Description based upon print version of record.
Linear-Quadratic Controls in Risk-Averse Decision Making; Preface; Contents; Chapter 1: Introduction; 1.1 Risk Assessment and Management; 1.2 Monograph Ideas and Contributions; 1.3 Methodology; 1.4 Chapter Organization; References; Chapter 2: Risk-Averse Control of Linear-Quadratic Tracking Problems; 2.1 Introduction; 2.2 The Problem; 2.3 The Value of Performance-Measure Statistics; 2.4 Statements of Mayer Problem with Performance Risk Aversion; 2.5 Risk-Averse Control of Adaptive Behavior; 2.6 Chapter Summary; References; Chapter 3: Overtaking Tracking Problems in Risk-Averse Control
3.1 Introduction3.2 Problem Description; 3.3 A Framework for Performance-Measure Statistics; 3.4 Statements of the Risk-Averse Control Problem; 3.5 Optimal Risk-Averse Tracking Solution; 3.6 Chapter Summary; References; Chapter 4: Performance Risk Management in Servo Systems; 4.1 Introduction; 4.2 The Performance Information Process; 4.3 The System Control Problem; 4.4 Statistical Optimal Control Solution; 4.5 Chapter Summary; References; Chapter 5: Risk-Averse Control Problems in Model-Following Systems; 5.1 Introduction; 5.2 Performance Information in Control with Risk Consequences
5.3 Formulation of the Control Problem5.4 Existence of Risk-Averse Control Solution; 5.5 Chapter Summary; References; Chapter 6: Incomplete Feedback Design in Model-Following Systems; 6.1 Introduction; 6.2 Backward Differential Equations for Performance-MeasureStatistics; 6.3 Performance-Measure Statistics for Risk-Averse Control; 6.4 Output-Feedback Control Solution; 6.5 Chapter Summary; References; Chapter 7: Reliable Control for Stochastic Systems with Low Sensitivity; 7.1 Introduction; 7.2 The Problem and Representations for Performance Robustness
7.3 Problem Statements with the Maximum Principle7.4 Low Sensitivity Control with Risk Aversion; 7.5 Chapter Summary; References; Chapter 8: Output-Feedback Control for Stochastic Systems with LowSensitivity; 8.1 Introduction; 8.2 Linking Performance-Measure Statistics and Risk Perceptions; 8.3 Statements Toward the Optimal Decision Problem; 8.4 Risk-Averse Control Solution in the Closed-Loop System; 8.5 Chapter Summary; References; Chapter 9: Epilogue; 9.1 The Aspect of Performance-Information Analysis; 9.2 The Aspect of Risk-Averse Decision Making; 9.3 What Will Be?; Index
Includes bibliographical references and index.
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.  .
Calculus of variations.
Computer mathematics.
Statistics .
Dynamics.
Ergodic theory.
Calculus of Variations and Optimal Control; Optimization.
Computational Science and Engineering.
Statistical Theory and Methods.
Dynamical Systems and Ergodic Theory.