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Predictor-Based Collision-Free and Connectivity-Preserving Resilient Formation Control for Multi-Agent Systems under Sensor Deception Attacks
  • Zhou Shu,
  • Qidong Liu,
  • Yang Yang
Zhou Shu
Nanjing University of Posts and Telecommunications
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Qidong Liu
Nanjing University of Posts and Telecommunications
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Yang Yang
Nanjing University of Posts and Telecommunications

Corresponding Author:[email protected]

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Abstract

Malicious attack is a potential threat for collision-free and connectivity-preserving formation control. In this paper, a predictor-based collision-free and connectivity-preserving resilient formation control strategy is presented for a class of nonlinear multi-agent systems under sensor deception attacks. The predictor states are designed to replace original states in the control strategy, and a novel attack compensator is constructed to suppress sensor deception attack. Prediction errors, instead of compromised errors, are introduced to update radial basis function neural networks (RBFNNs) weights. To achieve collision avoidance and connectivity preservation, a transformation function in logarithmic form is proposed. To avoid static and dynamic obstacles, an improved artificial potential function (APF) combined with their velocity information is constructed. Furthermore, to solve the local minimum in the combining of transformation function and APF, a virtual force is added to make agents get away. Based on the Lyapunov stability criterion, all closed-loop signals are bounded and all control objectives can be achieved. The simulation of a group of quadrotors has verified the effectiveness of the proposed resilient control strategy.
19 Jun 2023Submitted to International Journal of Robust and Nonlinear Control
26 Jun 2023Assigned to Editor
26 Jun 2023Submission Checks Completed
26 Jun 2023Review(s) Completed, Editorial Evaluation Pending
05 Jul 2023Reviewer(s) Assigned
15 Aug 2023Editorial Decision: Revise Minor