Broad-Learning-System-Based Model-Free Adaptive Predictive Control for Nonlinear MASs Under DoS Attacks

Abstract

In this paper, the containment control problem in nonlinear multi-agent systems (NMASs) under denial-of-service (DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent lin-earization model of NMASs. Then, a novel model-free adaptive predictive control (MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.