Adaptive Leaderless Consensus Control of Strict-Feedback Nonlinear Multiagent Systems With Unknown Control Directions

Abstract

The leaderless consensus problem over strict-feedback nonlinear multiagent systems (MASs) with unknown model parameters and control directions is investigated. The main idea of the existing consensus strategies for strict-feedback nonlinear MASs with unknown control directions is leading agents toward predefined global leaders/exosystems. However, in several missions, agents need to reach autonomous agreement on an a priori unknown quantity for a desired state, and hence the existing results are not applicable in these missions. The main contribution of this article is designing an adaptive leaderless consensus control scheme for strict-feedback nonlinear MASs when agents' control directions are unknown and unidentical. First, we introduce decentralized local error surfaces designed based on each agent position and neighboring agents' positions. We show that as the error surfaces remain bounded and converge to zero, the boundedness of the agents' positions and achieving leaderless consensus in the MAS can be guaranteed. Then, based on the properties of the Nussbaum-type functions, a decentralized backstepping adaptive control law is proposed under which the local error surfaces remain bounded and converge to zero. Finally, the design is more clarified and evaluated via an example.