Robust Nonlinear Model Predictive Control with Reduction of Uncertainty Via Robust Optimal Experiment Design

This paper studies the reduction of the conservativeness of robust nonlinear model predictive control (NMPC) via the reduction of the uncertainty range using guaranteed parameter estimation. Optimal dynamic experiment design is formulated in the framework of robust NMPC in order to obtain probing inputs that maximize the information content of the feedback and simultaneously to guarantee the satisfaction of the process constraints. We propose a criterion for optimal experiment design which provides a minimization of parameter uncertainty in the direction of improved performance of the process under robust economic NMPC. A case study from the chemical engineering domain is studied to show the benefits of the proposed approach.