This　work　presents　a　novel　nonlinear　direct　adaptive　data　cloud　based　fuzzy　controller　design　method　for　a　general　class　of　NARMAX　systems　employing　the　evolving　fuzzy　approximation.　In　the　proposed　fuzzy　controller,　dynamic　approximation　capability　is　performed　by　the　combined　action　of　structure　configuration　and　parameter　adjustment　using　multiple　strategies　of　recruiting,　pruning　and　updating　the　data　clouds,　and　self-tuning　the　consequent　parameters.　The　normalization　distance　between　two　local　densities　is　employed　to　measure　the　familiarity　of　each　two　data　clouds,　which　is　the　criteria　to　trigger　the　pruning　of　redundant　data　cloud　to　further　avoid　the　overlap　or　conflict.　The　simulation　results　demonstrate　low　computation　cost　and　favorable　tracking　performance.　©　2019　IEEE.