A　robust　soft　constrained　model　predictive　control　(RSCMPC)　method　is　proposed　to　address　the　effects　of　unknown　disturbances　for　wastewater　treatment　processes　(WWTPs).　The　disturbances　involving　inflow　fluctuation　and　noises　from　WWTPs　may　result　in　the　constraints　violation　of　MPC　due　to　its　uncertainty　of　bioprocess,　which　may　degrade　the　performance　of　the　steady　state.　First,　the　artificial　steady　state　is　introduced　to　mimic　the　nearest　feasible　steady　state　when　the　reference　steady　state　is　not　feasible.　The　deviation　caused　by　disturbances　between　the　artificial　steady　state　and　the　reference　steady　state　is　also　penalized　to　ensure　that　the　output　of　MPC　converges　to　the　reference　steady　state.　Second,　the　soft　constraints,　incorporating　two　slack　variables　and　a　penalty　term,　are　designed　to　relax　the　state　constraints　of　MPC　and　continuously　mitigate　the　constraint　violation,　thereby　ensuring　its　stability.　Third,　the　input　state　stability　(ISS)　under　disturbances　is　analyzed.　Finally,　the　simulation　tested　on　Benchmark　simulation　model　1　verifies　the　effectiveness　of　the　proposed　RSCMPC.　The　results　demonstrate　that　RSCMPC　improves　the　robustness　of　the　system　to　maintain　the　stable　operation　of　the　WWTPs.　Note　to　Practitioners-The　external　disturbances　of　wastewater　treatment　processes　(WWTPs)　will　result　in　the　constraints　violation　of　model　predictive　control　(MPC)　and　degrade　the　steady　state　performance.　To　overcome　the　influence　of　external　disturbances,　a　robust　soft　constrained　model　predictive　control　(RSCMPC)　method　is　designed.　This　method　mainly　includes　three　contributions:　First,　the　artificial　steady　state　is　constructed　with　the　prediction　derived　from　fuzzy　neural　network,　which　is　to　mimic　the　nearest　feasible　steady　state　when　the　reference　steady　state　is　not　feasible　under　disturbances.　Second,　the　soft　constrained　method　relaxes　the　state　constraints　of　MPC　through　two　slack　variables　to　compensate　the　effects　of　disturbances　and　restore　the　feasibility　of　the　controller.　Third,　the　input　state　stability　under　disturbances　is　analyzed.　Finally,　the　effectiveness　of　the　proposed　RSCMPC　method　is　evaluated　on　a　pilot　platform　of　a　real　WWTPs.　The　experimental　results　show　that　RSCMPC　can　improves　the　control　accuracy　and　robustness　of　the　system　under　external　disturbances.　The　proposed　RSCMPC　method　can　help　practitioners　improve　the　reliability　of　WWTPs　operation.