Observer-based consensus tracking of stochastic multi-agent systems via control Lyapunov function
Zheng Yan, Boqian Li, Ping Qi, Shiping Wen, Tingwen Huang · Neural Networks · 2025
This work discusses the consensus tracking for stochastic multi-agent systems with a leader-follower structure. An observer-based distributed control approach is put forward, leveraging control Lyapunov functions and quadratic programming framework. This control method ensures that each follower can track with the leader's state in the sense of expectation, even under stochastic disturbances. Two observers are given for each follower to observe its own state and the leader's state information, respectively, enabling fully distributed control requirement. Theoretical analysis guarantees the convergence of this control approach, and simulation results validate its effectiveness in achieving consensus tracking.