Neural-Network-Based Control With Dynamic Event-Triggered Mechanisms Under DoS Attacks and Applications in Load Frequency Control
Xueli Wang, Derui Ding, Xiaohua Ge, Hongli Dong
Abstract
The paper is concerned with the supplementary control based on adaptive dynamic programming (ADP) for a class of discrete-time networked system with the simultaneous presence of dynamic event-triggered mechanisms and Denial-of-Service (DoS) attacks. The dynamic behavior of DoSs is described by a model with the appropriate frequency and durations. A neural network (NN)-based observer is first designed to estimate system states in order to resolve the limitation in ADP-based control due mainly to data sparsity. The performance analysis and gain design of the NN-based observer are systematically discussed in light of the switched system theory combined with the average dwell-time method. Subsequently, the policy iteration algorithm with an actor-critic structure is developed to implement the designed supplementary ADP controller, and the corresponding condition on learning rates in weight updating rules is derived by virtue of the well-known Lyapunov stability. Finally, the effectiveness of the developed approach is demonstrated by an application in load frequency control of power systems.