Robust Model Predictive Control with Recursive State Estimation under Set-Membership Uncertainty

Tianchen Ji, Katherine Driggs-Campbell

Published 2020-08-11Version 1

The robustness against uncertainties is a critical part in control system design. While robust control typically incorporates process noise into the formulation, state estimate error is neglected, which may cause the controller to fail in real-world applications. This paper presents a robust model predictive controller with state estimation for constrained linear systems. Unknown but bounded disturbances and partial state information are considered. To handle the partial observability of the system states, a recursive state estimator is utilized to provide the state feedback and the bounds on state estimate error. The resulting controller is guaranteed to satisfy the hard constraints for all possible realizations of the process and measurement noise within the given sets. The effectiveness of the proposed algorithm is illustrated in a numerical example.