Securing nonlinear networked Systems with model-based event-triggered control

Abstract

This paper proposes a novel model-based event-triggered control (MB-ETC) strategy for nonlinear networked delta operator systems subject to deception-type cyber-attacks. The approach uniquely integrates MB-ETC with the delta operator framework—marking the first such integration for nonlinear systems operating under cyber threats. The method leverages a two-layer neural network (NN)-based nonlinear controller, which outperforms traditional single-layer radial basis function (RBF) networks by eliminating dependence on predefined basis functions and enhancing approximation capability. Control signals and NN weights are updated only when a carefully designed event-triggering condition is violated, significantly reducing communication load and conserving network resources. The controller also adapts dynamically to variations in system model dynamics across triggering intervals. To avoid Zeno behavior, a strictly positive lower bound on inter-event time is analytically derived. Simulation results validate the proposed method’s effectiveness and superiority in maintaining system stability under attack conditions while optimizing resource usage.