In　the　actual　industrial　production　process,　the　method　of　adaptively　tuning　proportional-integral-derivative　(PID)　parameters　online　by　neural　network　can　adapt　to　different　characteristics　of　different　controlled　objects　better　than　the　controller　with　PID.　However,　the　commonly　used　microcontroller　unit　(MCU)　cannot　meet　the　application　scenarios　of　real　time　and　high　reliability.　Therefore,　in　this　paper,　a　closed-loop　motion　control　system　based　on　BP　neural　network　(BPNN)　PID　controller　by　using　a　Xilinx　field　programmable　gate　array　(FPGA)　solution　is　proposed.　In　the　design　of　the　controller,　it　is　divided　into　several　sub-modules　according　to　the　modular　design　idea.　The　forward　propagation　module　is　used　to　complete　the　forward　propagation　operation　from　the　input　layer　to　the　output　layer.　The　PID　module　implements　the　mapping　of　PID　arithmetic　to　register　transfer　level　(RTL)　and　is　responsible　for　completing　the　output　of　control　amount.　The　main　state　machine　module　generates　enable　signals　that　control　the　sequential　execution　of　each　sub-module.　The　error　backpropagation　and　weight　update　module　completes　the　update　of　the　weights　of　each　layer　of　the　network.　The　peripheral　modules　of　the　control　system　are　divided　into　two　main　parts.　The　speed　measurement　module　completes　the　acquisition　of　the　output　pulse　signal　of　the　encoder　and　the　measurement　of　the　motor　speed.　The　pulse　width　modulation　(PWM)　signal　generation　module　generates　PWM　waves　with　different　duty　cycles　to　control　the　rotation　speed　of　the　motor.　A　co-simulation　of　Modelsim　and　Simulink　is　used　to　simulate　and　verify　the　system,　and　a　test　analysis　is　also　performed　on　the　development　platform.　The　results　show　that　the　proposed　system　can　realize　the　self-tuning　of　PID　control　parameters,　and　also　has　the　characteristics　of　reliable　performance,　high　real-time　performance,　and　strong　anti-interference.　Compared　with　MCU,　the　convergence　speed　is　far　more　than　three　orders　of　magnitude,　which　proves　its　superiority.