A　new　parameter-adjustable　radial　basis　function　(RBF)　neural　network　control　strategy　for　a　double-joint　manipulator　is　proposed　to　solve　the　problems　of　large　initial　error　in　the　error　approximation　process　and　long　time　to　reach　the　steady　state　in　the　trajectory　control　of　the　double-joint　manipulator.　Firstly,　the　central　parameter　of　the　RBF　neural　network　is　modified　by　a　gradient　descent　method,　so　that　the　parameter　can　be　adjusted　according　to　the　real-time　error　of　the　manipulator,　and　the　online　optimization　of　the　parameter　can　be　realized.　A　fuzzy　compensator　with　adjustable　input　boundary　is　proposed　to　reach　the　goal　that　the　actual　trajectory　of　the　manipulator　approaches　the　ideal　trajectory　better.　By　measuring　the　trajectory　error　and　the　derivative　of　the　error,　the　output　of　the　compensator　is　transferred　to　the　torque　control　module　after　fuzzy　reasoning,　so　that　the　output　torque　of　the　manipulator　is　closer　to　the　ideal　value.　Additionally,　a　genetic　algorithm　is　used　to　optimize　the　width　of　the　RBF　neural　network　function.　Simulation　results　show　that　after　the　control　torque　of　the　manipulator　is　adjusted　by　using　the　RBF　neural　network　control　method　with　adjustable　parameters,　the　accuracy　of　the　manipulator　control　process　is　improved　by　59%,　and　the　steady　time　of　the　manipulator　trajectory　tracking　is　shortened　by　69%.　©　2021,　Editorial　Office　of　Journal　of　Xi＇an　Jiaotong　University.　All　right　reserved.