Abstract
Strict consensus is difficult to be implemented due to the stochastic behavior of multi-agent systems (MASs), so a new concept, distribution consensus, is proposed here to keep the agents’ consensus in the stochastic sense, i.e., the output errors do not converge to a fixed value but follow a desired distribution function. The appropriate control protocol, with the output error probability density function (PDF) as the target, is designed based on the combination of sliding mode control and PDF compensation. Sliding mode control is the core part to ensure the whole system’s stability, and the PDF compensator is used to compensate the random variation and reduce the chattering effect, respectively. In order to realize the complete control in real time, the PDF compensator is modeling by a radial basis function (RBF) neural network and its optimal control law is calculated by the iterative training of RBF network weights. Finally, the effectiveness of the proposed method is verified by MASs simulations with three different communication topologies. The PDF compensator can greatly improve the consensus effect for the nonlinear stochastic MASs.
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