Distributed consensus tracking control of nonlinear multi-agent systems with dynamic output constraints and input saturation

Abstract

This paper studies the distributed output-feedback consensus tracking problem for nonlinear multi-agent systems (MASs) subject to dynamic output constraints and input saturation. Firstly, a neural-network (NN) based high gain observer is designed to provide estimation for unmeasured state of each agent. To address the dynamic output constraints problem, a unified barrier function is proposed to convert the constrained system into unconstrained one. Then, command filter-based backstepping is applied in design procedures to construct the distributed consensus tracking protocol and adaptive law. The auxiliary systems are constructed to generate additional error signals which are used to reduce the influence of input saturation. Thirdly, the boundedness of all signals in closed-loop MASs is proved based on Lyapunov stability theory. The consensus tracking error converges to an arbitrarily small neighborhood around the origin and the general dynamic output constraints are not violated under saturated input. Finally, The effectiveness of the proposed control protocol is verified using a simulation example.