Distributed adaptive consensus tracking control for uncertain non-linear multi-agent systems with input saturation

This study proposes a novel distributed adaptive consensus control scheme for a class of uncertain non-linear multi-agent systems with unknown control gains and input saturation. The radial basis function-neural networks are used to approximate the uncertain dynamics of the follower agents, as well as the effect of the neighbour agents of each agent. The dead-zone operator-based model is proposed to provide a smooth model of the saturation non-linearity. Then, the consensus strategy is proposed based on the minimal learning parameter algorithm and the dynamic surface control method. The stability analysis shows that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded and the consensus tracking error converges to a small vicinity of the origin. The proposed scheme solves the ‘singularity’ problem without using the projection operator. Furthermore, it avoids the ‘explosion of complexity’ and ‘explosion of learning parameters’ problems, simultaneously, and reduces the computational burden. Simulation results performed on a set of single-link robots composed of four followers and one leader verify the effectiveness of the proposed method.