An algorithm for constrained nonlinear optimization under uncertainty

This paper considers robust formulations for the constrained control of sys tems under uncertainty. The underlying model is nonlinear and stochastic. A mean-variance robustness framework is adopted. We consider formulations to ensure feasibility over the entire domain of the uncertain parameters. How ever, strict Feasibility may not always be possible, and can also be very e xpensive. We consider two alternative approaches to address feasibility. Fl exibility in the operational conditions is provided via a penalty framework . The robust strategies are rested on a dynamic optimization problem arisin g from a chemical engineering application. (C) 1999 Elsevier Science Ltd. A ll rights reserved.