Observer‐Based Adaptive Resilient Control for Nonlinear Cyber‐Physical Systems With Unknown Cyber‐Attacks: A Self‐Triggered Approach

ABSTRACT This paper studies the resilient control problem for a class of nonlinear cyber‐physical systems (CPSs) under sensor and actuator attacks. Different from those in the existing literature, both the additive and multiplicative cyber‐attacks imposed on the sensor and actuator in this paper are unknown and time‐varying. For the unavailable system states, an observer driven by compromised system input is constructed to estimate them. During the design procedure of backstepping control, some novel techniques are developed to deal with the additive and multiplicative sensor attacks. Additionally, two Nussbaum functions are introduced to solve the unknown control direction problem arising from uncertain sensor and actuator attacks. Meanwhile, a self‐triggered mechanism (STM) is employed to minimize communication overhead, enabling more efficient data transmission and reducing the need for continuous real‐time updates. Based on the state estimations and the compromised output signal, a new adaptive neural self‐triggered resilient control strategy is proposed for this class of systems via the combination of the backstepping technique and the neural network‐based approximation method. Stability analysis shows that all signals in the closed‐loop system are semi‐globally uniformly ultimately bounded (SGUUB) despite the existence of cyber‐attacks. Eventually, two simulation examples show that the proposed self‐triggered resilient control strategy can facilitate the endogenous security of closed‐loop systems under various sensor and actuator attacks and show the superiority in transfer efficiency and response time.