Linear-Quadratic Controls in Risk-Averse Decision Making

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.

Contenu

-1. Introduction. -2. Risk-Averse Control of Linear-Quadratic Tracking Problems. -3. Overtaking Tracking Problems in Risk-Averse Control. -4. Performance Risk Management in Servo Systems. -5. Risk-Averse Control Problems in Model-Following Systems. -6. Incomplete Feedback Design in Model-Following Systems. -7. Reliable Control for Stochastic Systems with Low Sensitivity. -8. Output Feedback Control for Stochastic Systems with Low Sensitivity. -9. Epilogue. Index.