2021/05/07 by Johannes Köhler, Matthias A. Müller, Köhler, Johannes +3
Engineering · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Electrical engineering #FOS: Mathematics #Fault Detection and Control Systems #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2105.03427
openalex publication_date 2021/05/07 · openalex created_date 2021/05/24 · openalex updated_date 2026/07/28
We present a framework to design nonlinear robust output feedback model predictive control (MPC) schemes that ensure constraint satisfaction under noisy output measurements and disturbances. We provide novel estimation methods to bound the magnitude of the estimation error based on: stability properties of the observer; detectability; set-membership estimation; moving horizon estimation (MHE). Robust constraint satisfaction is guaranteed by suitably incorporating these online validated bounds on the estimation error in a homothetic tube based MPC formulation. In addition, we show how the performance can be further improved by combining MHE and MPC in a single optimization problem. The framework is applicable to a general class of detectable and (incrementally) stabilizable nonlinear systems. While standard output feedback MPC schemes use offline computed worst-case bounds on the estimation error, the proposed framework utilizes online validated bounds, thus reducing conservatism and improving performance. We demonstrate the reduced conservatism of the proposed framework using a nonlinear 10-state quadrotor example.