Intelligent　control　strategies　can　significantly　enhance　the　efficiency　of　model　parameter　adjustment.　However,　existing　intelligent　motion　control　strategies　for　robotic　arms　based　on　the　broad　learning　system　lack　sufficient　accuracy　and　fail　to　account　for　the　effects　of　joint　motion　limitations　on　overall　control　performance.　To　address　the　aforementioned　challenges,　this　paper　proposes　a　robotic　arm　motion　control　strategy　based　on　a　deep　cascaded　feature-enhanced　Bayesian　broad　learning　system　with　motion　constraints　(MC-DCBLS).　Firstly,　the　motion　control　strategy　based　on　a　deep　cascaded　feature-enhanced　Bayesian　broad　learning　system　(DCBBLS)　is　designed,　which　simplifies　the　modeling　process　and　significantly　improves　control　accuracy.　Secondly,　the　motion　constraint　mechanism　is　introduced　to　optimize　the　control　strategy　to　ensure　that　the　robotic　arm　motion　does　not　break　through　the　physical　limit.　Finally,　the　parameter　constraints　of　the　control　strategy　network　were　obtained　by　introducing　the　Lyapunov　theory　to　ensure　the　stability　of　the　robotic　arm　motion　control.　The　effectiveness　of　the　proposed　control　strategy　was　validated　through　both　simulations　and　physical　experiments.　The　results　demonstrated　that　the　strategy　significantly　improved　the　accuracy　of　robotic　arm　motion　control,　with　the　root　mean　square　error　(RMSE)　in　position　tracking　reduced　to　0.038　rad.　This　represents　a　61.26%　reduction　in　error　compared　to　existing　techniques.　©　2025