Due　to　the　nonlinear　and　highly　time-varying　issues　of　wastewater　treatment　processes,　a　wastewater　treatment　control　method　based　on　adaptive　recurrent　fuzzy　neural　network　(RFNN)　is　proposed.　Firstly,　the　adaptive　RFNN　identifier　is　used　to　establish　the　nonlinear　dynamic　model　of　wastewater　treatment　process.　The　model　can　afford　the　state　variable　information　of　wastewater　treatment　process　to　RFNN　controller,　which　can　ensure　the　accuracy　of　manipulated　variable　is　adjusted　by　controller.　Secondly,　RFNN　identifier　and　RFNN　controller　are　learning　through　gradient　descent　algorithm　with　an　adaptive　learning　rate,　which　guarantee　the　convergence　of　learning　process　of　RFNN,　and　a　function　is　constructed　by　lyapunov　theory　to　prove　the　convergence　of　this　algorithm.　Finally,　the　simulation　experiment　carried　out　based　on　BSM1　platform.　Compared　with　PID,　model　predictive　control　and　forward　neural　network　control　techniques,　the　simulation　results　show　that　the　proposed　method　can　improve　obviously　the　control　accuracy　of　wastewater　treatment.　©　2016,　Editorial　Department　of　Control　Theory　&　Applications　South　China　University　of　Technology.　All　right　reserved.