In　this　paper,　the　versatile　value-iteration-based　control　method,　aimed　at　affine　systems　with　unknown　dynamics,　is　proposed　to　deal　with　the　optimal　tracking　control　problem.　Neural　networks　are　adopted　to　approximate　system　dynamics　and　a　novel　approach　is　presented　to　estimate　the　steady　state　control　input　based　on　the　established　identifier.　Additionally,　two　other　neural　networks,　called　the　critic　network　and　the　action　network,　are　used　to　implement　the　optimal　tracking　control　algorithm.　Finally,　based　on　the　proposed　method,　the　tracking　controller　is　designed　to　control　a　specific　simulation　example.　It　is　shown　that,　for　any　randomly　given　initial　state　vector,　the　controller　is　able　to　make　the　affine　system　track　the　reference　trajectory　without　knowing　the　system　dynamics.