Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems
  Tracking Control under Switching Topologies
Zewen Yang Songbo Dong Armin Lederer Xiaobing Dai Siyu Chen Stefan Sosnowski Georges Hattab Sandra Hirche
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.