摘要：AbstractWe present a real-time implementable robust Nonlinear Model Predictive Control (NMPC) framework that simultaneously addresses model uncertainty and unmeasured disturbances based on a multistage scenario tree to describe the evolution of uncertainties. The multistage scenario tree computes a control action that hedges against all possible uncertainty realizations and optimizes expected performance. However, the scenario tree structure inevitably increases the optimization problem size, as robust horizons become longer. This presents a challenge to solve large-scale problems in real-time. We propose a parallelizable advanced-step multistage NMPC (as-msNMPC) that precomputes a set of solutions in background so that the online computation effort is negligible. We apply an as-msNMPC framework to a CSTR example to show the controller’s robustness and improved online performance over competing robust NMPC methods.
关键词：KeywordsRobust Nonlinear Model Predictive ControlOptimal ControlDynamic OptimizationStochastic Programming
