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Neural-Network-based Event-triggered Adaptive Security Path Following Control of AGVs Subject to Abnormal Actuator Signal
  • Hong-Tao Sun,
  • Pengfei Zhang,
  • Chen Peng
Hong-Tao Sun
Qufu Normal University - Rizhao Campus
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Pengfei Zhang
Qufu Normal University - Rizhao Campus
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Chen Peng
Shanghai University School of Mechatronic Engineering and Automation

Corresponding Author:[email protected]

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The malicious physical attacks from both sensor and actuator side make real threats to the security and safety of autonomous ground vehicles (AGVs). This paper focuses on the problem of neural-network-based event-triggered adaptive security control (ET-ASC) scheme for path following of AGVs subject to arbitrary abnormal actuator signal. Firstly, we assume that an arbitrary abnormal signal is caused by arbitrary malicious attacks or disturbances from actuators. Then, radial basis function neural network (RBF-NN) is used to reconstruct such abnormal actuator signal. Secondly, modelling issues on security path following control of AGVs with Sigmoid-like ETC scheme are shown when the AGV is suffering from abnormal actuator signal. In what follows, an ET-ASC scheme is developed to mitigate the adverse effects of abnormal actuator signal with the reconstructed abnormal signal based on a novel Sigmoid-like event-triggered communication scheme. By using the proposed RBF-NN-based ET-ASC scheme, H ∞ control performance can be guaranteed under arbitrary malicious actuator signal rather than such attacks following a specific probability distribution. Finally, some simulation experiments are provided to verify the effectiveness of proposed ET-ASC scheme.
05 Jan 2023Submitted to International Journal of Robust and Nonlinear Control
06 Jan 2023Submission Checks Completed
06 Jan 2023Assigned to Editor
06 Jan 2023Review(s) Completed, Editorial Evaluation Pending
31 Jan 2023Reviewer(s) Assigned
26 Mar 2023Editorial Decision: Revise Minor
03 Apr 20231st Revision Received
04 Apr 2023Submission Checks Completed
04 Apr 2023Assigned to Editor
04 Apr 2023Review(s) Completed, Editorial Evaluation Pending
24 Apr 2023Reviewer(s) Assigned
23 May 2023Editorial Decision: Accept