In　this　paper,　we　develop　an　event-driven　robust　guaranteed　cost　control　strategy　of　continuous-time　(CT)　systems　via　improved　adaptive　critic　learning　(ACL).　First,　we　choose　a　suitable　cost　function　which　reflects　uncertainties,　control,　and　regulation,　in　order　to　transform　the　robust　control　problem　into　the　optimal　control　problem.　Then,　we　obtain　the　time-driven　optimal　control　law　and　the　Hamilton-Jacobi-Bellman　equation.　Next,　through　theoretical　analysis,　we　derive　the　event-driven　optimal　control　law　of　the　nominal　system　based　on　the　ACL　method,　and　prove　the　robust　stabilization　of　the　CT　nonlinear　system.　Additionally,　we　construct　a　novel　critic　neural　network　learning　algorithm　to　accelerate　the　convergence　of　weights.　We　also　obtain　the　neural-network-based　event-driven　condition　and　prove　the　closed-loop　system　stability.　Finally,　the　simulation　result　shows　the　effectiveness　of　the　event-driven　guaranteed　cost　control　design.　©　2022　IEEE.