This　article　presents　the　problem　of　autonomous　control　of　an　unmanned　aerial　manipulator　(UAM)　developed　for　operation　with　unknown　disturbances,　wherein　the　disturbances　from　the　coupling　effect　between　the　UAM　and　the　external　environment　need　to　be　considered.　Regarding　the　coupling　force　as　a　disturbance　to　the　entire　UAM　system,　an　adaptive　prescribed　performance　control　(APPC)　scheme　utilizing　the　knowledge　of　prescribed　performance　is　proposed　to　guarantee　the　transient　and　steady-state　performance　responses.　Also,　an　adaptive　law　is　designed　to　estimate　the　upper　boundary　parameters　of　the　UAM　system　uncertainties　and　disturbances,　wherein　the　restrictive　constant　boundary　assumptions　and　the　prior　information　of　the　upper　bound　are　not　required　in　the　controller　design.　Furthermore,　to　enable　safe　manipulation　in　a　realistic　situation,　an　end-effector　trajectory　generation　method　is　presented　satisfying　the　joint　angle　limitation.　For　the　validation　of　the　proposed　method,　the　simulation　results　of　numerical　simulation　comparisons　are　shown.　Moreover,　experimental　scenarios　including　stable　flight　and　simulated　co-work　with　humans　in　complex　environments　are　designed　to　verify　the　proposed　method.　Note　to　Practitioners-This　article　is　motivated　by　the　problem　of　aerial　manipulation　under　unknown　disturbances,　which　may　be　caused　by　the　wide　movement　of　the　manipulator　and　the　sudden　loading　or　unloading　of　an　object.　Existing　approaches　for　aerial　manipulation　often　require　the　assumption　of　a　constant　or　slowly　varying　external　disturbance.　However,　a　priori　bounded　disturbance　might　impose　a　priori　bound　on　the　system　state　before　obtaining　closed-loop　stability.　In　this　article,　the　proposed　controller　with　an　adaptive　law　is　designed　to　estimate　the　upper　boundary　parameters　of　the　overall　disturbances　and　ensure　the　predefined　performance,　so　that　the　prior　information　of　the　upper　bound　of　disturbances　is　not　required.　The　performance　of　the　proposed　control　strategy　is　demonstrated　via　numerical　simulation　comparisons　and　experiments,　including　stale　flight　and　simulated　co-work　with　humans　in　a　complex　environment.