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23 April 2020 Adaptive risk minimization for control and decision under uncertainty
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In this paper develop a novel, quantitative, rigorous and efficient method for risk minimization for control and decision under uncertainty. The crucial components of our approach include a rigorous, efficient risk evaluation method and a stochastic optimization technique. The risk evaluation method is an adaptive Monte Carlo estimation method which is derived from the concept of relative entropy and truncated inverse binomial sampling. The stochastic optimization technique is built upon evolutionary computing methods such as genetic algorithms, where the fitness function is constructed from the adaptive Monte Carlo estimation method. The effectiveness of the proposed method is demonstrated by its applications to the design of PID controllers for uncertain systems, where the probability of performance violation is minimized.
Conference Presentation
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Xinjia Chen, Adam Jannik, and Rafiqul Islam "Adaptive risk minimization for control and decision under uncertainty", Proc. SPIE 11425, Unmanned Systems Technology XXII, 114250Q (23 April 2020);

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