Output Sensitivity Modification for Nonlinear Model Predictive Control

We present the Output Sensitivity Modification (OSM) strategy for dynamic optimization and a corresponding Nonlinear Model Predictive Controller (NMPC): OSM-NMPC. The OSM strategy redefines the solutions of a dynamic optimization problem, and characterizes them by a set of halting conditions. These conditions are derived from a conceptual Modified-Gradient Sequential Quadratic Programming (MSQP) algorithm, which applies a modification to the model input-output sensitivity matrix at each step of an SQP algorithm applied to the dynamic optimization problem. Previous work has used sensitivity modifications in linear MPC to incorporate structural preferences such as input-output pairing/decoupling. These methods are limited to the linear case, and only assess performance and constraint violation with respect to the modified model. The OSM-NMPC scheme addresses this by extending the method to the nonlinear case, and evaluates feasibility using the true model. To illustrate the efficacy of OSM-NMPC, we apply the proposed method to a benchmark, unstable, exothermic, jacketed Continuous Stirred Tank Reactor (CSTR) process in a numerical case study, where we decouple the inlet flow rate from the tank temperature. The study also compares the OSM-NMPC controller to three other controllers that attempt to achieve decoupling by modifying the cost function.