With　rapid　advancements　in　digital　twin　technology　within　the　Industrial　Internet　of　Things,　integrating　digital　twins　with　industrial　robotic　arms　presents　a　promising　direction.　This　integration　promotes　the　remote　operation　and　intelligence　of　industrial　control　processes.　However,　the　control　and　error　management　of　robotic　arms　in　digital　twin　systems　pose　challenges.　In　this　paper,　we　present　a　digital　twin-empowered　robotic　arm　system　and　propose　a　control　policy　using　deep　reinforcement　learning,　specifically　the　proximal　policy　optimization　approach.　The　construction　and　functionality　of　each　subsystem　within　the　digital　twin-empowered　robotic　arm　control　system　are　detailed.　To　address　errors　caused　by　mechanical　structure　and　virtual-real　mapping　in　the　digital　twin,　an　integrated　proximal　policy　optimization　and　fuzzy　PID　approach　is　proposed.　Experimental　results　demonstrate　that　proximal　policy　optimization　is　adaptable　to　virtual-real　mapping　errors,　while　the　fuzzy　PID　method　corrects　physical　errors　quickly　and　accurately.　The　robotic　arm　can　reach　the　target　point　using　this　integrated　approach.　Overall,　error　management　problems　in　digital　systems　have　been　well　addressed,　and　our　scheme　can　provide　an　accurate　and　adaptive　control　strategy　for　the　robotic　arm.