In　this　paper,　robust　control　problems　are　investigated　for　nonlinear　continuous-time　systems.　A　momentum-based　gradient　descent　(GD)　approach　is　developed　to　enhance　the　convergence　performance　of　parameters　in　adaptive　dynamic　programming　(ADP).　By　introducing　the　idea　of　momentum,　the　oscillation　in　the　process　of　GD　is　alleviated　and　the　selection　of　the　learning　rate　becomes　more　flexible.　Under　the　framework　of　ADP,　the　robust　control　problem　is　transformed　into　the　optimal　control　problem　by　modifying　the　cost　function.　To　avoid　limitations　of　the　initial　admissible　condition,　an　additional　term　is　employed　in　the　computation　of　the　current　gradient.　Based　on　the　online　policy　iteration　algorithm,　the　momentum-based　GD　approach　is　constructed　as　an　improved　learning　algorithm　to　optimize　the　critic　network　weights.　Finally,　a　simulation　is　conducted　to　verify　the　effectiveness　of　the　established　learning　strategy.