Nonlinear Chance-Constrained Process Optimization under Uncertainty

By M. Wendt, A. Li, Günter Wozny. 2002

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Robust multi-objective optimal control of uncertain (bio)chemical processes (2011, background): “Sometimes stochastic information about the occurrence of uncertainties is available such that chance constraints can be formulated (Wendt et al., 2002; Mitra, 2009).”
A tractable approximation of non-convex chance constrained optimization with non-Gaussian uncertainties (2015, methodology): “For the sake of comparison, the same problem is solved with the previously proposed back-mapping approach of Wendt, Li, and Wozny (2002) and the analytic approximation strategy, for various lengths of the prediction horizon h.”
Risk-Sensitive Reinforcement Learning Applied to Control under Constraints (2005, background): “Wendt, Li, &Wozny, 2002 ), but can be achieved with our RL approach.”
Chance Constrained Programming for Optimal Power Flow Under Uncertainty (2011, methodology): “On the other hand, in previous studies using the back-mapping approach for solving CCP problems [8], [9], it is assumed that a global monotonic relation between and a selected is a prerequisite, i.”
Chance constrained programming approach to process optimization under uncertainty (2008, methodology): “A computation method for one single probabilistic constraint is proposed by Wendt et al. (2001). The basic idea of this method is to map the uncertain output distribution to that of the uncertain input variables.”