With　the　increase　of　urbanization　rate,　the　problem　of　water　shortage　and　pollution　is　more　and　more　serious.　It　is　important　to　improve　the　efficiency　of　wastewater　treatment　to　protect　the　urban　ecological　environment.　The　wastewater　treatment　process　involves　a　variety　of　biochemical　reactions　and　has　strong　time-vary　dynamics.　The　concentration　design　in　the　wastewater　treatment　process　can　be　regarded　as　a　tracking　control　problem　for　a　class　of　nonlinear　systems.　In　order　to　solve　this　problem,　this　paper　develops　an　intelligent　control　method　with　tracking　goal　representation　heuristic　dynamic　programming　(T-GrHDP)　by　combining　the　GrHDP　with　a　novel　tracking　framework.　A　model　network　is　built　by　using　a　dataset　consisting　of　real　input　and　output　data　of　the　controlled　object,　which　can　overcome　the　dependence　on　the　system　dynamic.　In　order　to　improve　the　learning　efficiency　of　the　proposed　algorithm,　we　introduce　the　goal　network　to　provide　more　effective　information　for　the　critic　network.　The　classical　actor-critic　scheme　in　reinforcement　learning　is　used　to　obtain　the　approximate　optimal　control　strategy.　By　introducing　some　necessary　lemmas　and　assumptions,　the　convergence　of　the　proposed　algorithm　is　proved.　Finally,　the　T-GrHDP　method　is　successfully　applied　in　two　industrial　simulations　including　the　wastewater　treatment　system.