In　this　paper,　we　aim　to　solve　the　optimal　tracking　control　problem　for　a　class　of　nonaffine　discrete-time　systems　with　actuator　saturation.　First,　a　data-based　neural　identifier　is　constructed　to　learn　the　unknown　system　dynamics.　Then,　according　to　the　expression　of　the　trained　neural　identifier,　we　can　obtain　the　steady　control　corresponding　to　the　reference　trajectory.　Next,　by　involving　the　iterative　dual　heuristic　dynamic　programming　algorithm,　the　new　costate　function　and　the　tracking　control　law　are　developed.　Two　other　neural　networks　are　used　to　estimate　the　costate　function　and　approximate　the　tracking　control　law.　Considering　approximation　errors　of　neural　networks,　the　stability　analysis　of　the　proposed　algorithm　for　the　specific　systems　is　provided　by　introducing　the　Lyapunov　approach.　Finally,　via　conducting　simulation　and　comparison,　the　superiority　of　the　developed　optimal　tracking　method　is　confirmed.　Moreover,　the　trajectory　tracking　performance　of　the　wastewater　treatment　application　is　also　involved　for　further　verifying　the　proposed　approach.　(C)　2021　Elsevier　Ltd.　All　rights　reserved.