Adaptive neural network event-triggered secure formation control of nonholonomic mobile robots subject to deception attacks
Year: 2024
Type: article
Abstract: This paper investigates the adaptive neural network (NN) event-triggered secure formation control problem for nonholonomic mobile robots (NMRs) subject to deception attacks. The NNs are employed to approximate unknown nonlinear functions in robotic dynamics. Since the transmission channel from sensor-to-controller is vulnerable to deception attacks, a NN estimation technique is introduced to estimate the unknown deception attacks. In order to alleviate the amount of communication between controller-and-actuator, an event-triggered mechanism with relative threshold strategy is established. Then, an adaptive NN event-triggered secure formation control method is proposed. It is proved that all closed-loop signals of controlled systems are bounded and the formation tracking errors converge a neighborhood of the origin in the presence of deception attacks. The comparative simulations illustrate the effectiveness of the proposed secure formation control scheme.
Authors: Kai Wang, Wei Wu, Shaocheng Tong
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Award: G37309136
OpenAlex ID: W4404056072