Linear-Quadratic Controls in Risk-Averse Decision Making: by Khanh D. Pham

By Khanh D. Pham

Linear-Quadratic Controls in Risk-Averse determination Making cuts throughout regulate engineering (control suggestions and selection optimization) and records (post-design functionality research) with a typical topic: reliability bring up noticeable from the responsive perspective of incorporating and engineering multi-level functionality robustness past the long-run ordinary functionality into regulate suggestions layout and selection making and complicated dynamic structures from the beginning. This monograph presents an entire description of statistical optimum regulate (also referred to as cost-cumulant keep an eye on) idea. up to speed difficulties and themes, emphasis is basically put on significant advancements attained and specific connections among mathematical records of functionality value determinations and choice and keep watch over optimization. bankruptcy summaries make clear the relevance of built effects, which makes this monograph appropriate for graduate-level lectures in utilized arithmetic and electric engineering with systems-theoretic focus, non-compulsory research or a reference for readers, researchers, and graduate scholars who're drawn to theoretical constructs and layout ideas for stochastic managed platforms.

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Additional info for Linear-Quadratic Controls in Risk-Averse Decision Making: Performance-Measure Statistics and Control Decision Optimization

Example text

In: Proceedings of the 2nd European Conference on Structural Control (2000) 11. F. Jr. First generation seismic-AMD benchmark: Robust structural protection by the cost cumulant control paradigm. In: Proceedings of American Control Conference, pp. 1–5 (2000) 12. F. : Third generation windAMD benchmark: Cost cumulant control methodology for wind excited tall buildings. In: Proceedings of the 14th ASCE Engineering Mechanics Conference (2000) 13. F. : The role and use of optimal cost cumulants for protection of civil structures.

And D once the Notice that the product system uniquely determines H , D, admissible affine input l and feedback gain K are specified. Henceforth, they are ˘ K, l) and D = D(·, K, l). 7). The greater the higher-order performance-measure statistics, the greater the degrees of performance uncertainty or performance risks. Similarly, the degrees of uncertainty of one probability distribution of a random performance measure are greater than another probability distribution if the linear combination of first finite numbers of performance-measure statistics of the first distribution is greater than that of the second distribution.

0). ˘ and D once the Notice that the product system uniquely determines H , D, admissible affine input l and feedback gain K are specified. Henceforth, they are ˘ K, l) and D = D(·, K, l). 7). The greater the higher-order performance-measure statistics, the greater the degrees of performance uncertainty or performance risks. Similarly, the degrees of uncertainty of one probability distribution of a random performance measure are greater than another probability distribution if the linear combination of first finite numbers of performance-measure statistics of the first distribution is greater than that of the second distribution.