To　achieve　precise　lateral　dynamics　control　for　electric　and　autonomous　vehicles　under　system　model　uncertainties　and　unknown　external　disturbances,　this　paper　introduces　a　novel　barrier　function-based　neural　network　(NN)　adaptive　integral　sliding　mode　control　for　multi-axle　independent　steering　vehicles　(MISVs).　Initially,　NNs　are　designed　to　approximate　the　unknown　parameters　associated　with　uncertainties　in　the　MISV　dynamics　model.　Subsequently,　an　adaptive　integral　sliding　mode　control　based　on　barrier　function　is　developed　to　ensure　robust　vehicle　state　tracking.　Unlike　traditional　adaptive　sliding　mode　controllers,　the　proposed　approach　guarantees　rapid　convergence　of　variables　within　a　finite　time,　even　without　prior　knowledge　of　the　upper　bounds　of　unknown　external　disturbances,　thereby　significantly　reducing　vibration　and　oscillation　phenomena.　Finally,　the　Lyapunov　stability　theory　is　applied　to　rigorously　demonstrate　that　the　closed-loop　system　of　the　MISV　remains　stable　within　finite　time.　The　efficacy　of　the　proposed　controller　is　validated　through　hardware-in-the-loop　experiments,　with　results　indicating　a　reduction　in　maximum　tracking　errors　for　the　desired　yaw　rate　and　sideslip　angle　by　8.24%　and　40.44%,　respectively,　compared　to　the　conventional　control　method.　　©　2015　IEEE.