https://pubmed.ncbi.nlm.nih.gov/38117630
This study presents a data-driven event-triggered adaptive dynamic programming control approach for nonlinear systems with input saturation, using a modified index and dynamic penalty factor to improve error convergence and reduce redundant triggering events.