Adaptive　inverse　control,　proposed　by　Bernard　Widrow,　is　mainly　based　on　the　well-known　least　mean　square　(LMS)　algorithm.　The　LMS　is　a　stochastic　gradient　algorithm　under　the　minimum　mean　square　error　(MSE)　criterion,　which　performs　well　for　linear　and　Gaussian　systems.　However,　its　performance　will　become　poor　when　signals　are　non-Gaussian,　especially　when　systems　are　disturbed　by　impulsive　noises.　In　this　work,　in　order　to　improve　the　robustness　of　the　adaptive　inverse　control　against　impulsive　noises,　we　propose　a　new　adaptive　inverse　control　method,　which　is　based　on　the　recently　developed　maximum　correntropy　criterion　(MCC)　algorithm.　The　MCC　algorithm　aims　at　maximizing　the　correntropy　between　the　model　output　and　the　desired　response.　Since　correntropy　is　a　nonlinear　similarity　measure　that　contains　higher-order　statistics　of　the　signals　and　is　insensitive　to　large　outliers,　the　proposed　method　can　achieve　desirable　performance　in　impulsive　noise　environments.　Theoretical　results　on　optimal　solution　and　convergence　are　derived.　Simulation　results　are　also　presented　to　demonstrate　the　superior　performance　of　the　new　method.　©　2015