Linear-quadratic controls in risk-averse decision making : by Khanh D. Pham

By Khanh D. Pham

1. advent -- 2. Risk-averse keep an eye on of linear-quadratic monitoring difficulties -- three. Overtaking monitoring difficulties in risk-averse regulate -- four. functionality hazard administration in servo structures -- five. Risk-averse regulate difficulties in model-following structures -- 6. Incomplete suggestions layout in model-following structures -- 7. trustworthy regulate for stochastic structures with low sensitivity -- eight. Output-feedback keep an eye on for stochastic structures with low sensitivity -- nine. Epilogue

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Extra info for Linear-quadratic controls in risk-averse decision making : performance-measure statistics and control decision optimization

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32) where V ε , Y , Z˘ , Z is the value function. Proof. With the aid of the recent development [7, 8], the proof then follows for the results stated here. 5 Optimal Risk-Averse Tracking Solution It is obvious that the optimization problem considered herein is in “Mayer form”. It is therefore solved by applying an adaptation of the Mayer-form verification theorem of dynamic programming given in [7]. In the language of dynamic programming, it requires to parameterize all starting times and states of a family of optimization problems as ε , Y , Z˘ , Z .

It should also be noted that, although the optimization criterion of the statistical optimal control problem represents a competition among cumulant values, the ultimate objective herein is to introduce parametric designs of freedom in the class of feedback control laws which will result from the problem solution. These parametric designs of freedom have been exploited to achieve desirable closed-loop system properties. Finally, the general solution of the statistical optimal control problem for the class of linearquadratic tracking systems is presented and is determined by a feedback statistical optimal control obtained by a set of coupled Riccati-type differential equations and time-dependent tracking variables found by solving an auxiliary set of coupled differential equations (incorporating the desired trajectory) backward from a stable final time.

Obviously, many interesting optimization problems involve closely guided tracking criteria. For instance, a class of overtaking tracking problems is central to the study of physical systems as it is to the synthesis of feedback systems that are able to track a-priori scheduling signals and target control references. Interested readers may consult [1–3] to appreciate the scope of the concepts involved in designing feedback controls for deterministic systems that optimize quadratic performance indices of reference signals.

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