In　this　paper,　based　on　the　discounted　generalized　value　iteration,　an　intelligent　algorithm　is　designed　to　address　optimal　tracking　control　problems　for　a　class　of　complex　nonlinear　systems.　By　choosing　an　appropriate　initial　value,　the　iterative　cost　function　converges　to　the　optimum　in　a　monotonically　decreasing　form.　In　the　light　of　the　monotonically　decreasing　value　iteration　algorithm,　we　discuss　the　admissibility　properties　of　the　iterative　tracking　control　law　and　the　asymptotic　stability　of　the　error　system　with　different　discounted　factors.　For　facilitating　the　implementation　of　the　algorithm,　a　data-driven　model　network　is　established　to　learn　the　unknown　system.　The　critic　and　action　networks　are　constructed　to　approximate　the　cost　function　and　compute　the　iterative　tracking　control　law.　It　is　worth　noting　that　a　new　termination　criterion　is　developed　to　guarantee　the　effectiveness　of　the　iterative　tracking　control　law.　The　termination　criterion　contains　two　conditions.　The　first　condition　is　used　to　ensure　the　validity　of　the　tracking　control　law,　which　is　helpful　to　evaluate　the　stability　of　the　error　system.　The　second　condition　is　adopted　to　guarantee　the　near-optimal　properties　of　the　tracking　control　law.　Finally,　two　experimental　examples　are　conducted,　where　a　wastewater　treatment　application　is　involved,　in　order　to　demonstrate　the　control　performance　of　the　proposed　near-optimal　tracking　control　method.　Copyright　©2022　Acta　Automatica　Sinica.　All　rights　reserved.