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In many control problems, not all states can be measured and the system is subject to parametric uncertainties, measurement noise, and hard input constraints. To tackle such problems for linear systems, we propose to combine a recursive parameter and state estimator based on Bayes' theorem with a stochastic model predictive control approach. To efficiently obtain the probability density functions...
One purpose of the ?Historical Perspectives? column is to look back at work done by pioneers in control and related fields that has been neglected for many years but was later revived in the control literature. This column discusses the topic of Norbert Wiener?s most cited paper, which proposed polynomial chaos expansions (PCEs) as a method for probabilistic uncertainty quantification in nonlinear...
This paper studies the model predictive control of dynamic systems subject to stochastic parametric uncertainty due to plant/model mismatches and exogenous disturbance that corresponds to uncertain circumstance in operation of the system. Model and disturbance uncertainties are ubiquitous in any mathematical models of system and control theory. Parametric uncertainty propagation or quantification...
Uncertainties are ubiquitous in mathematical models of complex systems and this paper considers the incorporation of generalized polynomial chaos expansions for uncertainty propagation and quantification into robust control design. Generalized polynomial chaos expansions are more computationally efficient than Monte Carlo simulation for quantifying the influence of stochastic parametric uncertainties...
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