Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies
This work addresses robust tracking control for multi-agent systems with uncertain dynamics and dynamic communication, offering a promising solution for applications like robotics or autonomous vehicles, though it appears incremental as it builds on existing cooperative and learning-based methods.
The paper tackled the tracking control problem for Euler-Lagrange multi-agent systems with partially unknown dynamics under switching topologies by developing a cooperative learning approach using Gaussian process regression, resulting in bounded tracking errors with high probability as validated through simulation experiments.
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.