Semi-global robust tracking consensus for multi-agent uncertain systems with input saturation via metamorphic low-gain feedback

Abstract

This paper proposes a metamorphic adaptive low-gain feedback approach to investigate the semi-global robust tracking consensus problem of multi-agent uncertain systems with input saturation under a directed communication topology. The main contribution is to develop a new robust tracking consensus low-gain feedback algorithm, in which the feedback gain design does not rely on eigenstructure assignment algorithm and the solution of a parametric Algebraic Riccati Equation (ARE). By introducing appropriate assumptions, a class of metamorphic adaptive low-gain feedback protocols is designed based on the limited states information among neighbors. It is proved, in the sense of Lyapunov, that the robust tracking consensus problem for closed-loop multi-agent uncertain systems with input saturation and directed communication topology can be solved. Furthermore, the results are extended to the semi-global robust tracking consensus problem of nonlinear multi-agent uncertain systems with nonlinear input saturation under a jointly connected directed communication topology. Finally, two examples are presented to demonstrate the effectiveness of the proposed theories. ,