Data-Driven Event-Triggered Control for Discrete-Time Neural Networks Subject to Actuator Saturation

Abstract

In this article, the data-driven event-triggered control is addressed for unknown discrete-time neural networks (DTNNs) under actuator saturation and external perturbation. The research problem is raised due to the following two reasons: 1) a practical system is often affected by external perturbations and it is costly to acquire an accurate system model; 2) the network bandwidth and the control inputs are always constrained due to physical hardware. To handle the above issues, the methodology is to first establish a model-based stability condition under the designed saturated event-triggered controller and then to transform the model-based stability condition into a data-based stability condition relying only on the perturbation-corrupted data via the extended S-lemma. The key results are: 1) a data-based DTNNs system representation is presented by collecting perturbation-corrupted state-input data. Then, a data-based stability criterion is derived and the saturated event-triggered controller is designed without an explicit system model; 2) an optimization method is presented that can maximize the estimation of attractor (EoA) and minimize the estimated domain of attraction (DoA) simultaneously. Finally, the effectiveness of the proposed approach is illustrated and some quantitative analyses are offered by two numerical examples.