Robust predictive control with data-based multi-step prediction models

摘要

In this paper a novel method for the design of MPC controllers based on multi-steps ahead external representation system models is proposed. These models are assumed to be identified from available data, e.g., using the set-membership approach, that computes a guaranteed estimate of the model uncertainty bounds. Exploiting this information, the proposed algorithm guarantees input and output constraint satisfaction, recursive feasibility, and robust convergence properties. A simulation case study is shown to demonstrate the effectiveness of the proposed approach.