Trajectory Bounds of Input-to-State Stability for Nonlinear Model Predictive Control∗. Issue 8 (2015) - Griffith, Devin W.; Biegler, Lorenz T.
Abstract: Model predictive control (MPC) is an optimization-based tool that is widely used in the chemical industry, and nonlinear MPC (NMPC) expands the technology to handle more detailed models that are accurate across a wider range of state values. Many works in the literature have studied NMPC using Input-to-State Stability (ISS). The purpose of this work is to provide a method for calculating state trajectory bounds for NMPC using ISS theory. These predictive bounds are derived in terms of parameters that may be found from a series of open loop calculations in the general nonlinear case. Results are shown for a scalar linear system and a nonlinear CSTR, and the challenges involved with higher dimensional problems are discussed.