Distributed Adaptive Tracking Control of Pure-feedback Multi-agent Systems with Full State and Control Input Constraints

The distributed tracking control of multi-agent systems with the general pure-feedback agent dynamics, the full state constraints, and the control input constraints is investigated in this paper. Based on the one-to-one nonlinear mapping, the saturation function transformation, and the degree elevation techniques, the pure-feedback multi-agent system with full state constraints and control input constrains is firstly transformed into a novel one without constraints. Considering the unknown control sign and the unknown dynamic models, a distributed adaptive control law is proposed by leveraging the merits of Nussbaum function and neural networks. The rigorous Lyapunov stability analysis shows that the agent constraints are always satisfied, and the cooperative tracking errors can be made as small as possible by appropriately setting the control parameters. Finally, a numerical simulation is conducted to clarify the effectiveness of the proposed control strategy.