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Event-Triggered Prescribed-Time Resilient Control of Euler-Lagrange Systems With Deception Attacks and Deferred Constraints
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DOI：10.1002/rnc.70614.png)
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Abstract
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In this paper, an event-triggered prescribed-time resilient control scheme for uncertain Euler-Lagrange (EL) systems with deception attacks and deferred constraints is presented. First, to mitigate the effects of false data injection (FDI) attacks in the sensor channel, a novel coordinate transformation and the Nussbaum gain technique are applied under the framework of the backstepping method. The unknown actuator attacks are compensated by the application of a radial basis function neural network (RBFNN). Then, distinguishing from most existing resilient control algorithms that do not consider the transient characteristics of the output signal, the prescribed-time performance functions (PTPFs) and a barrier Lyapunov function (BLF) are merged in this paper, so that the output signal can converge to an adjustable set within a predefined time. To guarantee that the system satisfies the deferred constraint, a transformation function is introduced. What's more, we construct an improved event-triggered mechanism (ETM), which can utilize communication resources more efficiently. Finally, simulation results based on a two-link manipulator model are depicted to showcase the effectiveness of the proposed method.
Keywords:
barrier Lyapunov function
deception attacks
deferred constraints
Euler-Lagrange systems
event-triggered control
resilient control
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