A Novel Robust Decentralized Adaptive Fuzzy Control for Swarm Formation of Multiagent Systems
In this paper, a novel decentralized adaptive control scheme for multiagent formation control is proposed based on an integration of artificial potential functions with robust control techniques. Fully actuated mobile agents with partially unknown models are considered, where an adaptive fuzzy logic system is used to approximate the unknown system dynamics. The robust performance criterion is used to attenuate the adaptive fuzzy approximation error and external disturbances to a prescribed level. The advantages of the proposed controller can be listed as robustness to input nonlinearity, external disturbances, and model uncertainties, and applicability on a large diversity of autonomous systems. A Lyapunov-function-based proof is given of robust stability, which shows the robustness of the controller with respect to disturbances and system uncertainties. Simulation results are demonstrated for a swarm formation problem of a group of six holonomic robots, illustrating the effective attenuation of approximation errors and external disturbances, even in the case of agent failure. Moreover, experimental results confirm the validity of the presented approach and are included to verify the applicability of the scheme for a swarm of six real holonomic robots.