Adaptive neural consensus tracking control of distributed nonlinear multiagent systems with unmodeled dynamics
Hao Jiang, Wei Su, Ben Niu, Huanqing Wang, Jiaming Zhang · International Journal of Robust and Nonlinear Control · 2022
Summary This paper aims to address the adaptive consensus tracking control problem for distributed nonlinear multi‐agent systems with unmodeled dynamics. It should be emphasized that each considered follower is modeled as a nonlinear non‐strict feedback system in which the control gains are unknown functions rather than constants. By applying an inherent property of radial basis function (RBF) neural networks (NNs) and the introduced dynamics signals, the design difficulties aroused from unknown nonlinearities and unmodeled dynamics are overcome such that the control purpose can be achieved. Then, based on adaptive backstepping methods, a new consensus tracking control protocol is proposed. It is shown that the closed‐loop systems are stable and all the outputs of followers ultimately track the reference signal, that is, the output of the leader, synchronously. Finally, the effectiveness of the proposed control protocol is illustrated through the simulation results.