Adaptive Consensus Control of Nonlinear Multiagent Systems With Unknown Control Directions Under Stochastic Topologies
Hamed Rezaee, Farzaneh Abdollahi · IEEE Transactions on Neural Networks and Learning Systems · 2017
The consensus problem over high-order nonlinear multiagent systems with the Brunovsky-type model is studied. The model parameters and control directions of agents are supposed to be unknown. Hence, based on Nussbaum-type functions, an adaptive protocol is proposed, which guarantees achieving consensus in the network when the parameters and control directions of the agents are unknown and unidentical. The main contribution of this paper (compared with the existing similar results in the literature) is to guarantee achieving consensus in networks of agents when the communication topology is not connected constantly, and communication links stochastically switch over time. It is shown that if the probability of the network connectivity is not zero, under some conditions, almost sure consensus can be achieved. Illustrative examples verify the accuracy of the proposed consensus protocol.