A　direct　adaptive　dynamic　neural　network　control　(DADNNC)　method　is　proposed　to　control　the　dissolved　oxygen　concentration　in　the　wastewater　treatment　process.　The　established　control　system　mainly　includes　a　neural　controller　and　a　compensate　controller.　The　neural　controller　fulfills　the　mapping　between　the　system　states　and　control　variable　using　the　fuzzy　neural　network,　which　can　adjust　the　structure　and　parameters　simultaneously.　A　novel　pruning　algorithm　is　presented　based　on　the　useless　rate　of　the　rules,　and　the　convergence　while　adding　and　pruning　neurons　is　guaranteed　theoretically.　Further,　the　compensation　controller　is　designed　for　decreasing　the　approximating　error　introduced　by　the　neural　network,　and　the　parameter　update　law　is　deduced　by　the　Lyapunov　theorem.　Finally,　the　simulation　results,　based　on　the　international　benchmark　simulation　platform,　show　that　the　proposed　method　can　achieve　better　control　accuracy　and　superior　adaptive　ability　compared　with　neural　network　controller　with　fixed　structure,　PID　controller　and　model　predictive　control　method.　©,　2015,　South　China　University　of　Technology.　All　right　reserved.