Force　optimization　of　multifingered　robotic　hands　can　be　formulated　as　a　convex　programming　or　non-convex　programming　problem.　This　paper　presents　a　neural　dynamical　method　for　real-time　optimal　control　of　force　distribution.　Compared　with　existing　approaches　to　the　force　optimization,　the　proposed　neural　dynamical　approach　is　shown　to　have　a　lower　model　complexity　and　an　exponential　convergence　even　in　the　case　of　singular　positions.　A　simulation　example　shows　that　the　proposed　neural　dynamical　approach　can　achieve　optimal　force　distribution　in　real　time.