Distributed Error-Identification and Correction using Block-Sparse Optimization

TL;DR

This paper introduces a distributed framework for fault detection and correction in multi-agent systems using block-sparse optimization, combining SCP and ADMM methods for efficient sparse vector reconstruction from nonlinear measurements.

Contribution

It develops a novel distributed multi-agent FDIR algorithm that handles various inter-agent measurements for fault identification and state recovery, advancing decentralized fault management.

Findings

Successfully identifies faulty agents using inter-agent distances.

Recovers true states of faulty agents through sparse error vector reconstruction.

Demonstrates effectiveness in a numerical multi-agent scenario.

Abstract

The conventional solutions for fault-detection, identification, and reconstruction (FDIR) require centralized decision-making mechanisms which are typically combinatorial in their nature, necessitating the design of an efficient distributed FDIR mechanism that is suitable for multi-agent applications. To this end, we develop a general framework for efficiently reconstructing a sparse vector being observed over a sensor network via nonlinear measurements. The proposed framework is used to design a distributed multi-agent FDIR algorithm based on a combination of the sequential convex programming (SCP) and the alternating direction method of multipliers (ADMM) optimization approaches. The proposed distributed FDIR algorithm can process a variety of inter-agent measurements (including distances, bearings, relative velocities, and subtended angles between agents) to identify the faulty agents and recover their true states. The effectiveness of the proposed distributed multi-agent FDIR approach is demonstrated by considering a numerical example in which the inter-agent distances are used to identify the faulty agents in a multi-agent configuration, as well as reconstruct their error vectors.