Cooperative Estimation for Synchronization of Heterogeneous Multi-Agent Systems Using Relative Information
For multi-agent systems researchers, this work offers an incremental improvement in distributed estimation scalability and robustness.
The paper presents a distributed estimation algorithm for heterogeneous multi-agent systems using only relative measurements, which improves scalability by limiting agents to estimating local and neighbor states. The algorithm ensures robust performance against disturbances and integrates with output synchronization.
In this paper, we present a distributed estimation setup where local agents estimate their states from relative measurements received from their neighbours. In the case of heterogeneous multi-agent systems, where only relative measurements are available, this is of high relevance. The objective is to improve the scalability of the existing distributed estimation algorithms by restricting the agents to estimating only their local states and those of immediate neighbours. The presented estimation algorithm also guarantees robust performance against model and measurement disturbances. It is shown that it can be integrated into output synchronization algorithms.