Aiming　at　the　motion　balance　control　problem　of　a　two-wheeled　upright　robot　and　combining　OCPA　(operant　conditioning　probabilistic　automaton)　bionic　learning　system,　a　robust　bionic　learning　control　scheme　based　on　fuzzy　basis　function　is　designed.　It　doesn＇t　require　prior　knowledge　of　dynamic　system　or　off-line　learning　phase.　The　architecture　of　the　robust　bionic　learning　controller　contains　a　bionic　learning　unit,　a　gain　controller　unit　and　a　robust　adaptive　control　unit.　The　bionic　learning　unit　is　realized　based　on　fuzzy　basis　function　network　(FBFN),　and　it　is　not　only　employed　to　generate　operant　action　for　approximating　nonlinear　parts,　but　also　to　evaluate　the　operant　action.　Based　on　the　error　measurement　signal　provided　by　performance　measurement　mechanism,　the　orientation　information　is　generated　to　tune　operant　action　generation　network.　The　function　of　the　gain　controller　unit　is　to　guarantee　the　stability　and　performance　of　system,　and　the　function　of　the　robust　adaptive　unit　is　to　eliminate　the　approximation　error　of　the　FBFN　and　external　disturbances.　Besides,　the　proposed　scheme　can　significantly　shorten　the　learning　time　and　guarantee　the　stability　of　system　by　on-line　tuning　all　parameters　of　FBFN　based　on　Lyapunov　stability　theory.　The　stability　of　the　robust　bionic　learning　controller　is　proved　in　theory,　and　its　feasibility　and　effectiveness　can　be　demonstrated　from　the　results　of　simulation　experiment.