Advanced-multi-step Moving Horizon Estimation

Abstract

Abstract Moving Horizon Estimation (MHE) is an important optimization-based approach for state estimation and parameter updates, because of its capabilities in dealing with nonlinearity and state constraints. In addition, one of the applications is to provide the full state information for Model Predictive Controller (MPC) to control the process in either setpoint tracking or economic control purposes. However, the computational burden of MHE could deteriorate the control performance if the feedback delay caused by computation is too long, leading to potential safety issues or process damage. In this paper, we propose a fast moving horizon estimation algorithm to overcome the long computational time of MHE for real-time control applications, especially for fast dynamics or large-scale systems. We exploit the nonlinear programming (NLP) sensitivity and make use of efcient NLP solvers, IPOPT and k_aug, to reduce the on-line computational costs. This new approach is demonstrated on a CSTR process, where results are compared to ideal MHE and advanced-step MHE (asMHE).