Event-triggered output feedback tracking against random deception attacks for nonlinear systems

Abstract

In this paper, the scheme of event-triggered output feedback tracking control is investigated for nonlinear systems with input/output filters when there exist random deception attacks. The success rate of the random deception attacks is represented by a Bernoulli process, and the successful attack is to inject false data into the output or input signals. To address this issue, event-triggered mechanisms are adopted in the sensor-to-controller (SC) and controller-to-actuator (CA) channels to reduce the utilization of network resources and the frequency of effective attacks. Moreover, two filters (the input and output filters) are designed to mitigate the impact of the random attacks and enable the backstepping method applicable by rendering discontinuous triggering signals differentiable. Sufficient conditions for tracking effectiveness are provided by using the Lyapunov stability theory. Finally, a simulation example verifies the validity and practicality of the proposed methods.