Intelligent　control　methods　have　led　to　a　significant　simplification　of　the　robotic　arm　modeling　and　control　tuning　process,　and　thus　they　have　been　widely　used.　To　further　improve　the　precision　of　robotic　arm　motion　control,　this　paper　proposes　a　robotic　arm　motion　control　strategy　based　on　a　cascaded　feature-enhanced　elastic-net　broad　learning　system　(CFE-EN-BLS).　This　will　fully　extract　data　features　to　improve　motion　control　accuracy.　Moreover,　ElasticNet　regression　is　introduced　to　reduce　feature　redundancy.　Finally,　Lyapunov　stability　theory　is　introduced　to　constrain　the　learning　parameters　of　the　proposed　learning　method　to　enhance　the　convergence　of　the　control　strategy.　The　simulation　and　experiment　show　that　the　proposed　control　strategy　can　realize　high-precision　trajectory　tracking　control　of　the　robotic　arm.　©　2024　IEEE.