This　paper　proposes　an　adaptive　self-learning　fuzzy　autopilot　design　for　uncertain　bankto-turn　(BTT)　missiles　due　to　external　disturbances　and　system　errors　from　the　variations　of　the　aerodynamic　coefficients　and　control　surface　loss.　The　self-learning　fuzzy　systems　called　extended　sequential　adaptive　fuzzy　inference　systems　(ESAFISs)　are　utilized　to　compensate　for　these　uncertainties　in　an　adaptive　backstepping　architecture.　ESAFIS　is　a　real-time　self-learning　fuzzy　system　with　simultaneous　structure　identification　and　parameter　learning.　The　fuzzy　rules　of　the　ESAFIS　can　be　added　or　deleted　based　on　the　input　data.　Based　on　the　Lyapunov　stability　theory,　adaptation　laws　are　derived　to　update　the　consequent　parameters　of　fuzzy　rules,　which　guarantees　both　tracking　performance　and　stability.　The　robust　control　terms　with　the　adaptive　boundestimation　schemes　are　also　designed　to　compensate　for　modeling　errors　of　the　ESAFISs　by　augmenting　the　self-learning　fuzzy　autopilot　control　laws.　The　proposed　autopilot　is　validated　under　the　control　surface　loss,　aerodynamic　parameter　perturbations,　and　external　disturbances.　Simulation　study　is　also　compared　with　a　conventional　backstepping　autopilot　and　a　neural　autopilot　in　terms　of　the　tracking　ability.　The　results　illustrate　that　the　designed　fuzzy　autopilot　can　obtain　better　steady-state　and　transient　performance　with　the　dynamically　self-learning　ability.