Data-driven fault-tolerant consensus for multiagent systems under switching topology

For multiagent systems with switching topology, the data-driven fault-tolerant consensus is investigated in this work. The actuator faults are directly estimated using the proposed enhanced-RBFNN-based fault estimation algorithm (ERFEA). Different from the previous method, the utilization of NN approximations in the weight update law is avoided and training process persists throughout commencement of system running until occurrence of fault is overcame. To reach consensus, a distributed model free adaptive fault-tolerant consensus control technique is created based on ERFEA. In contrast to previous approaches, the constructed fault-tolerant control mechanism just requires the input/output data of agents and does not impose the requirement of a fixed topology. The created method’s efficiency is finally validated by a simulation.