Secure bipartite tracking control of a class of nonlinear multi-agent systems with nonsymmetric input constraint against sensor attacks

Abstract

This paper addresses a secure bipartite tracking control problem for a class of nonlinear multi-agents (MASs) with nonsymmetric input constraints. In the presence of adversarial sensor attacks, a secure measurement preselector, along with an explicit sufficient condition, and a neural network (NN) secure state observer are introduced for achieving secure state estimation. Then, a secure bipartite tracking control strategy is proposed, where observation predictors are designed to reconstruct prediction errors in such a way as to improve control performance. Furthermore, an auxiliary system is presented to eliminate influence from nonsymmetric input saturations. It is theoretically proved that the proposed control strategy not only guarantees bipartite tracking of the MAS but also preserves the stability of the resulting closed-loop system in spite of senor attacks. Finally, two illustrative examples are presented to verify the effectiveness of the obtained results.