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Polytopic model of uncertain system is considered as a plant model to be controlled.
A model of uncertain reasoning based on a form of logical argumentation appears to unify many diverse ideas.
Thus the core of this paper is to discuss under what conditions we can convert the expected value model of uncertain variational inequality problems into deterministic variational inequality problems.
By introducing the expected value model of uncertain variables (vector), we converted one class of uncertain variational inequality problems into a class of deterministic variational inequality problems, which can be solved by many classical methods such as those presented in [1, 22].
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The paper presents a fractional moment method for probabilistic lifetime modelling of uncertain engineering systems.
In particular, we study the modeling of uncertain systems, robust stability analysis for systems with unstructured uncertainty, robustness analysis for systems with structured uncertainty, and robust control system design including H∞ control methods.
Model validation of uncertain structures is a challenging research focus because of uncertainties involved in modeling, manufacturing processes, and measurement systems.
In a typical JCP model, all of uncertain constraints required being satisfied at a joint probability level, which increases robustness in controlling overall system risk during the optimization process.
The modification is based on the model assumption of uncertain measurement times.
We notably assess the effect on the model predictions of uncertain values for some of the parameters that are difficult or time-consuming to measure (such as the protein or mRNA half-lives).
After having introduced and formalized the framework of uncertain systems modeling and the different performance metrics, the issues of sensitivity analysis and surrogate modeling are addressed.
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