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

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 …

agent algorithm communication control correlation correlations cs.lg cs.ma dynamics framework gaussian processes multi-agent predictions process processes regression systems tracking uncertainty work