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

arXiv: https://arxiv.org/abs/2402.03048 · HTML: https://mlsanity.com/paper/2402.03048/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Distributed Gaussian Processes](https://mlsanity.com/paper/1502.02843/) (2015)
- [Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control](https://mlsanity.com/paper/1906.01376/) (2019)
- [Feedback Linearization based on Gaussian Processes with event-triggered Online Learning](https://mlsanity.com/paper/1911.06565/) (2019)
- [Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning](https://mlsanity.com/paper/2305.08169/) (2023)
- [Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes](https://mlsanity.com/paper/2103.15929/) (2021)
- [Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version](https://mlsanity.com/paper/2304.05138/) (2023)
- [Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller](https://mlsanity.com/paper/2303.09828/) (2023)
- [Distributed Global Output-Feedback Control for a Class of Euler-Lagrange Systems](https://mlsanity.com/paper/1907.00220/) (2019)

## Built on by (most cited first, 4 of 4)

- [Quality or Quantity? Error-Informed Selective Online Learning with Gaussian Processes in Multi-Agent Systems: Extended Version](https://mlsanity.com/paper/2601.14275/) (2026)
- [Prompt2Auto: From Motion Prompt to Automated Control via Geometry-Invariant One-Shot Gaussian Process Learning](https://mlsanity.com/paper/2509.14040/) (2025)
- [Asynchronous Cooperative Online Learning for Multi-Robot Control under Computational Delays](https://mlsanity.com/paper/2608.29562/) (2026)
- [COIN-GP: Cooperative Online Learning in Networked Distributed Systems with Partial Measurements via Gaussian Process Regression](https://mlsanity.com/paper/2609.20598/) (2026)

## Abstract (arXiv)

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.