In　this　paper,　a　data-based　optimal　tracking　control　approach　is　developed　by　involving　the　iterative　dual　heuristic　dynamic　programming　algorithm　for　nonaffine　systems.　In　order　to　gain　the　steady　control　corresponding　to　the　desired　trajectory,　a　novel　strategy　is　established　with　regard　to　the　unknown　system　function.　Then,　according　to　the　iterative　adaptive　dynamic　programming　algorithm,　the　updating　formula　of　the　costate　function　and　the　new　optimal　control　policy　for　unknown　nonaffine　systems　are　provided　to　solve　the　optimal　tracking　control　problem.　Moreover,　three　neural　networks　are　used　to　facilitate　the　implementation　of　the　proposed　algorithm.　In　order　to　improve　the　accuracy　of　the　steady　control　corresponding　to　the　desired　trajectory,　we　employ　a　model　network　to　directly　approximate　the　unknown　system　function　instead　of　the　error　dynamics.　Finally,　the　effectiveness　of　the　proposed　method　is　demonstrated　through　a　simulation　example.　Copyright　(C)　2020　The　Authors.