In　this　article,　a　novel　neuro-optimal　tracking　control　approach　is　developed　toward　discrete-time　nonlinear　systems.　By　constructing　a　new　augmented　plant,　the　optimal　trajectory　tracking　design　is　transformed　into　an　optimal　regulation　problem.　For　discrete-time　nonlinear　dynamics,　the　steady　control　input　corresponding　to　the　reference　trajectory　is　given.　Then,　the　value-iteration-based　tracking　control　algorithm　is　provided　and　the　convergence　of　the　value　function　sequence　is　established.　Therein,　the　approximation　error　between　the　iterative　value　function　and　the　optimal　cost　is　estimated.　The　uniformly　ultimately　bounded　stability　of　the　closed-loop　system　is　also　discussed　in　detail.　Moreover,　the　iterative　heuristic　dynamic　programming　(HDP)　algorithm　is　implemented　by　involving　the　critic　and　action　components,　where　some　new　updating　rules　of　the　action　network　are　provided.　Finally,　two　examples　are　used　to　demonstrate　the　optimality　of　the　present　controller　as　well　as　the　effectiveness　of　the　proposed　method.