Real-Time Optimization via Modifier Adaptation using Partial Models

A. PAPASAVVAS; T. DE AVILA FERREIRA; A. G. MARCHETTI; D. BONVIN

Toulouse

Congreso; IFAC World Congress 2017; 2017

International Federation of Automatic Control (IFAC)

Modier adaptation is a real-time optimization method that has the ability to reach the plant optimum upon convergence despite the presence of uncertainty in the form of plant-model mismatch and disturbances. The approach is based on modifying the cost and constraint functions predicted by the model by means of appropriate rst-order correction terms. The main diculty lies in the fact that these correction terms require the cost and constraint gradients of the plant to be estimated from experimental data at each iteration. The model used can support a signicant level of approximation. However, it must satisfy the following two requirements: (i) a model adequacy condition related to the second-order optimality conditions must be valid at the plant optimum, and (ii) the model must have the same input variables as the plant. In this paper, we consider the case where (ii) is not veried because only a partial or incomplete model is available. We propose to approximate the unmodeled part of the system by a linear model that is identied using the same perturbed operating points that are used in modier adaptation for gradient estimation. The approach is illustrated through the simulated example of a reaction-separation system with recycle.