Learning-based　control　strategies　can　significantly　streamline　the　process　of　modeling　robotic　arms　and　adjusting　control　parameters,　making　them　widely　used　in　robotic　arm　motion　control.　However,　the　existing　learning-based　motion　control　strategies　suffer　from　insufficient　feature　extraction,　resulting　in　limited　prediction　accuracy.　To　address　this　problem,　this　paper　proposes　a　robotic　arm　motion　control　strategy　based　on　a　cascaded　feature-enhanced　elastic-net　broad　learning　system　(CFE-EN-BLS),　which　improves　the　trajectory　tracking　accuracy　of　robotic　arms.　Firstly,　a　motion　control　strategy　of　the　cascaded　feature-enhanced　broad　learning　system　(CFE-BLS)　is　constructed　to　fully　extract　data　features　to　improve　joint　position-tracking　accuracy.　Secondly,　combined　with　elastic-net　regression,　a　motion　control　strategy　for　the　robotic　arm　based　on　CFE-EN-BLS　is　designed　to　reduce　feature　redundancy.　Finally,　the　learning　parameters　of　the　proposed　control　strategy　are　constrained　by　incorporating　Lyapunov　theory　to　bolster　the　convergence　of　the　control　strategy.　Simulation　and　experimental　results　show　that　the　proposed　control　strategy　can　effectively　extract　data　features　and　achieve　high-precision　trajectory　tracking　control　of　the　robotic　arm.　The　position　tracking　mean-squared-error　(MSE)　and　root-mean-squared-error　(RMSE)　are　0.00174rad　and　0.04167rad,　respectively,　which　represent　reductions　of　74.71%　and　49.76%　compared　to　the　existing　method.　©　2025　Elsevier　Ltd