Robust Estimation and Control for Heterogeneous Multi-agent Systems Based on Decentralized k-hop Prescribed Performance Observers

TL;DR

This paper introduces decentralized k-hop prescribed performance observers for heterogeneous multi-agent systems, enabling local estimation of states and inputs with guaranteed transient error bounds, even with limited neighbor communication.

Contribution

It presents a novel decentralized observer design that estimates states and inputs across multiple hops, with conditions to omit input estimation, ensuring robust control in multi-agent systems.

Findings

Establishes conditions for observer omission without losing convergence.

Demonstrates that estimated states enable system objectives with small errors.

Validates approach through simulation results.

Abstract

We propose decentralized k-hop Prescribed Performance State and Input Observers for heterogeneous multi-agent systems subject to bounded external disturbances. In the proposed input/state observer, each agent estimates the state and input of agents located two or more hops away using only local information exchanged with 1-hop neighbors, while guaranteeing that transient estimation errors satisfy predefined performance bounds. Conditions are established under which the input observer can be omitted, allowing the state observer convergence to be independent of the input estimates. Theoretical analysis demonstrates that if a closed-loop controller with full state knowledge achieves the control objective and the estimation-based closed-loop system is set-Input to State Stable (set-ISS) with respect to the goal set, then the estimated states can be used to achieve the system objective with an arbitrarily small worst-case error governed by the accuracy of the states estimates. Simulation results are provided to validate the proposed approach.