Distributed bounded-error state estimation for partitioned systems based on practical robust positive invariance

2013/11/18 by Stefano Riverso, Riverso, S., Daria Rubini +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.1311.4306

openalex publication_date 2013/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

We propose a partition-based state estimator for linear discrete-time systems composed by coupled subsystems affected by bounded disturbances. The architecture is distributed in the sense that each subsystem is equipped with a local state estimator that exploits suitable pieces of information from parent subsystems. Moreover, differently from methods based on moving horizon estimation, our approach does not require the on-line solution to optimization problems. Our state-estimation scheme, that is based on the notion of practical robust positive invariance developed in Rakovic 2011, also guarantees satisfaction of constraints on local estimation errors and it can be updated with a limited computational effort when subsystems are added or removed.

Citations

• Hycon2 Benchmark: Power Network System

Related

• Plug-and-play distributed state estimation for linear systems
• Iterative distributed moving horizon estimation of linear systems with penalties on both system disturbances and noise
• Partition-based Distributed Kalman Filter with plug and play features
• Moving horizon partition-based state estimation of large-scale systems -- Revised version
• Optimization-based State Estimation under Bounded Disturbances