This　paper　focuses　on　the　problem　of　the　time-varying　constrained　direct-current　(DC)　motor　system　with　input　saturation.　The　radial　basis　function　neural　network　with　less　parameters　approach　is　introduced　as　an　identifier　to　estimate　the　unknown　dynamics　in　DC　motor　system.　To　avoid　repeated　verification　of　the　feasibility　conditions　on　the　virtual　control,　the　nonlinear　mapping　is　employed　in　each　step　of　backstepping　procedure,　the　prescribed　transient　performance　on　tracking　error　as　well　as　the　constraint　on　system　states　are　directly　achieved　even　when　the　actuator　saturation　is　taken　into　account.　Based　on　the　Lyapunov　analysis,　the　developed　control　strategy　can　ensure　that　all　the　closed-loop　signals　are　bounded,　the　constraints　on　full　system　states　and　tracking　error　are　achieved.　The　simulation　example　is　used　to　illustrate　the　effectiveness　of　the　developed　control　strategy.