Cooperative Fuzzy Adaptive Control for Stochastic Nonlinear Multi-Agent Systems Using Artificial Potential Functions

A realistic control strategy for stochastic multi-agent systems (MAS) should consider uncertainties such as unknown dynamics of the agents, limited accessibility of the leader state, and external disturbances. Here, we propose an adaptive fuzzy control structure for a class of stochastic uncertain MAS that have partially unknown heterogeneous higher-order dynamics of the agents. Artificial potential functions (APFs) are incorporated to model the communication among the agents, and the unknown dynamics are approximated by the aid of fuzzy logic systems. The current work extends our previous work to the case where only some of the agents could access the states of the leader to reach an n-dimensional consensus on the leader states. The overall system’s stability is assured using Lyapunov stability theory, and all of the closed-loop signals of the system are shown to be semi-globally uniformly ultimately bounded (SGUUB) in the mean square. Simulation results ensure the capability of the system in low tracking error with reasonable control input usage.