In　this　paper,　a　novel　self-triggered　optimal　tracking　control　method　is　developed　based　on　the　online　action-　critic　technique　for　discrete-time　nonlinear　systems.　First,　an　augmented　plant　is　constructed　by　integrating　the　system　state　with　the　reference　trajectory.　This　transformation　redefines　the　optimal　tracking　control　design　as　the　optimal　regulation　issue　of　the　reconstructed　nonlinear　error　system.　Subsequently,　under　the　premise　of　ensuring　the　controlled　system　stability,　a　self-sampling　function　that　depends　solely　on　the　sampling　tracking　error　is　devised,　thereby　determining　the　next　triggering　instant.　This　approach　not　only　effectively　reduces　the　computational　burden　but　also　eliminates　the　need　for　continuous　evaluation　of　the　triggering　condition,　as　required　in　traditional　event-based　methods.　Furthermore,　the　developed　control　method　can　be　found　to　possess　excellent　triggering　performance.　The　model,　critic,　and　action　neural　networks　are　constructed　to　implement　the　online　critic　learning　algorithm,　enabling　real-time　adjustment　of　the　tracking　control　policy　to　achieve　optimal　performance.　Finally,　an　experimental　plant　with　nonlinear　characteristics　is　presented　to　illustrate　the　overall　performance　of　the　proposed　online　self-triggered　tracking　control　strategy.