The　wastewater　treatment　is　an　effective　method　for　alleviating　the　shortage　of　water　resources.　In　this　article,　a　data-driven　iterative　adaptive　tracking　control　approach　is　developed　to　improve　the　control　performance　of　the　dissolved　oxygen　concentration　and　the　nitrate　nitrogen　concentration　in　the　nonlinear　wastewater　treatment　plant.　First,　the　model　network　is　established　to　obtain　the　steady　control　and　evaluate　the　new　system　state.　Then,　a　nonquadratic　performance　functional　is　provided　to　handle　asymmetric　control　constraints.　Moreover,　the　new　costate　function　and　the　tracking　control　policy　are　derived　by　using　the　dual　heuristic　dynamic　programming　algorithm.　In　the　present　control　scheme,　two　neural　networks　are　constructed　to　approximate　the　costate　function　and　the　tracking　control　law.　Finally,　the　feasibility　of　the　proposed　algorithm　is　confirmed　by　applying　the　designed　strategy　to　the　wastewater　treatment　plant.