The　control　of　internal　flow　and　external　carbon　is　crucial　for　the　municipal　wastewater　denitrification　process.　However,　due　to　the　disturbance　and　interactions　in　the　process,　it　is　difficult　to　achieve　suitable　control　performance.　To　solve　this　problem,　a　robust　multivariable　control　(RMC)　scheme　is　proposed　to　improve　the　process　control　efficiency.　First,　a　mechanism-based　control　method　is　designed　to　provide　an　explicit　control　signal　that　mitigates　the　effect　of　load　changes.　Second,　a　robust　control　method,　using　a　fuzzy　neural　network　sliding　mode　controller,　is　developed　to　improve　the　tracking　accuracy.　Third,　an　adaptive　learning　algorithm　is　proposed　to　tune　the　parameters　of　RMC　so　that　the　closed-loop　system　is　stable　in　the　term　of　Lyapunov　stability　theory.　Finally,　the　benchmark　simulations　of　municipal　wastewater　denitrification　process　demonstrate　that,　compared　with　other　control　strategies,　the　proposed　method　yields　a　stable　control　performance　with　an　obvious　energy　saving　effect.　©　2021　IEEE.