Offset-free Nonlinear Model Predictive Control Based on Moving Horizon Estimation for an Air Separation Unit

Air separation units (ASU) pose a classic problem for nonlinear system control. This paper proposes a framework that integrates nonlinear model predictive control (NMPC) and moving horizon estimation (MHE). We prove that the proposed method achieves offset free regulatory behavior, even in the presence of plant-model mismatches. If the plant uncertainty structure is known, the proposed framework can be modified to estimate the uncertainty parameters. Thus, the model used in the NMPC and MHE can be adaptively modified online. Finally, the proposed method is applied on a large scale air separation unit, and the steady state offset free behavior is observed.