Event‐Triggered Prescribed‐Time Resilient Control of Euler‐Lagrange Systems With Deception Attacks and Deferred Constraints

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.