Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies

TL;DR

This paper introduces a novel Gaussian process-based cooperative learning method for multi-agent Euler-Lagrange systems, enabling robust tracking control under switching communication topologies with high efficiency and stability.

Contribution

It develops a correlation-aware cooperative algorithm that efficiently captures inter-agent correlations without heavy computations, ensuring stability and bounded errors.

Findings

Effective tracking control demonstrated in simulations

High probability of bounded tracking errors

Efficient computation of aggregation weights

Abstract

This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies. The approach leverages a correlation-aware cooperative algorithm framework built upon Gaussian process regression, which adeptly captures inter-agent correlations for uncertainty predictions. A standout feature is its exceptional efficiency in deriving the aggregation weights achieved by circumventing the computationally intensive posterior variance calculations. Through Lyapunov stability analysis, the distributed control law ensures bounded tracking errors with high probability. Simulation experiments validate the protocol's efficacy in effectively managing complex scenarios, establishing it as a promising solution for robust tracking control in multi-agent systems characterized by uncertain dynamics and dynamic communication structures.