Consensus for Multi-Agent Systems under Output Constraints and Unknown Control Directions

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.