Consensus for Multiagent Systems Under Output Constraints and Unknown Control Directions
Yuan Sun, Bing Yan, Peng Shi, Cheng‐Chew Lim
Abstract
In this article, we design an adaptive leader–follower consensus controller for a class of nonlinear multiagent systems in the presence of time-varying asymmetric output constraints and unknown control directions. A new state transformation approach is introduced for each agent to transform the output into an equivalent unconstrained state. An adaptive neural network-based backstepping control method and a Nussbaum function approach are integrated to design the leader–follower consensus controller that compensates for the unknown control directions and guarantees that the consensus tracking error converges to a small compact set. Examples are given to demonstrate the effectiveness of the proposed new design techniques.