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An asymmetric time-varying Barrier Lyapunov Function (BLF) is employed to ensure constraint satisfaction.
The distinct feature of the robust OED approach is the inclusion of chance constraints to ensure constraint satisfaction in a stochastic setting.
The CF-MLSM has been shown to ensure constraint feasibility regardless of the nonlinearity of the constraint function, the feasible bounds, and the random characteristics during the meta-model-based RBDO process.
The method of CF-MLSM based RBDO has been shown to not only ensure constraint feasibility in a case where the meta-model-based RBDO process is employed, but also to require low expense, as compared with both conventional MLSM and non-approximate RBDO methods.
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Constraint (7) ensures constraint 6 will only be tested once for each pair of requests.
Using this parameter ensures constraint 6 is tested only once for each pair of requests.
To handle parametric model uncertainty, we present an adaptive controller that ensures constraint satisfaction during the transient phase of online parameter adaptation.
This paper investigates the application of stochastic nonlinear model predictive control (NMPC) to a thin film deposition process in the presence of model-plant mismatch while ensuring constraints at a specific probability limit.
The second bar represents the extended CUE constraint matrix used to ensure the constraints (a) and (d) of (49), where F is a K c × N all.
The third bar represents the extended DUE constraint matrix used to ensure the constraints (b) and (e) of (49), where N is a K d × N all.
Consequently, the DICV based direct interval optimization algorithm cannot ensure the constraint robustness of the optimal solution.
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