Interconnected Observers for Robust Decentralized Estimation with Performance Guarantees and Optimized Connectivity Graph
Yuchun Li, Ricardo G. Sanfelice

TL;DR
This paper introduces interconnected observers for linear systems that enhance robustness and convergence speed by optimizing the connectivity graph, outperforming traditional observers under noise and convergence constraints.
Contribution
It proposes a novel interconnected observer design that improves robustness and convergence speed, with optimized graph structures and performance guarantees.
Findings
Relaxation of the tradeoff between convergence rate and noise amplification.
Conditions ensuring local observer performance surpasses standard Luenberger observers.
Optimization frameworks for designing interconnected observers with specific convergence and robustness criteria.
Abstract
Motivated by the need of observers that are both robust to disturbances and guarantee fast convergence to zero of the estimation error, we propose an observer for linear time-invariant systems with noisy output that consists of the combination of N coupled observers over a connectivity graph. At each node of the graph, the output of these interconnected observers is defined as the average of the estimates obtained using local information. The convergence rate and the robustness to measurement noise of the proposed observer's output are characterized in terms of bounds. Several optimization problems are formulated to design the proposed observer so as to satisfy a given rate of convergence specification while minimizing the gain from noise to estimates or the size of the connectivity graph. It is shown that that the interconnected observers relax the well-known…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStability and Control of Uncertain Systems · Stability and Controllability of Differential Equations · Adaptive Control of Nonlinear Systems
