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By using a stochastic system analysis approach and average dwell time technique, a framework for the transmission power scheduling and control co-design is established.
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Steuer et al. [ 19] used a stochastic system of linear equations based on an underlying metabolic network of biochemical reactions to discover correlations between metabolite fluctuations around a steady-state.
In the SEn-BS system, energy flow behavior of the SEn-BS system is modeled using a stochastic queue.
The surveillance system was evaluated using a stochastic scenario tree model.
We therefore study the low copy number dynamics of this model, using a stochastic kinetics framework based on the Gillespie algorithm, and propose statistical and analytic techniques which help us analyse the equilibrium behaviour of our stochastic system.
This implies using a stochastic imputation.
Generalized polynomial chaos (gPC) expansions allow us to represent the solution of a stochastic system using a series of polynomial chaos basis functions.
This framework is based on multilayer modelling of a stochastic system using hierarchical viewpoints.
A stochastic system identification method was used to estimate the mechanical impedance of the mechanism's ankle in DP and IE directions, Z DP (f) and Z IE (f), respectively.
Then, we propose a stochastic version of this model using a system of stochastic ordinary differential equations that are in the form of the original model.
By using stochastic system theory, sufficient conditions on the existence of the desired event-based H∞ state feedback controller are derived such that the resultant event-based stochastic networked control system is exponentially stable in the mean square sense with a prescribed H∞ performance while reducing the number of sampled-state transmission along the feedback link as much as possible.
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