In　this　paper,　an　adaptive　control　scheme　based　on　command-filtered　backstepping　technique　is　developed　for　a　class　of　uncertain　multiple-input-multiple-output　(MIMO)　strict-feedback　nonlinear　systems.　Within　this　scheme,　extreme　learning　machine　(ELM)　with　random　hidden　nodes　is　used　in　the　controller　to　approximate　unknown　functions,　and　a　smooth　projection　algorithm　is　adopted　to　adjust　online　estimated　parameters　in　the　controller　such　that　the　boundedness　of　the　parameter　estimates　can　be　ensured.　Furthermore,　some　stale　command　filters　are　designed　to　produce　virtual　control　signals　and　their　derivations　such　that　the　analytic　calculation　of　the　partial　derivatives　of　virtual　control　signals　is　removed.　Also,　some　other　stable　filters　are　proposed　to　generate　compensating　signals　of　above-mentioned　filtered　errors　to　compute　compensated　tracking　errors.　It　is　proved　that　the　proposed　control　scheme　can　guarantee　the　boundedness　of　all　signals　in　the　closed-loop　system.　Finally,　the　proposed　control　scheme　is　applied　to　control　wind　turbine　and　the　simulation　studies　illustrate　the　theoretic　results　obtained.　©　2020　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.