In　this　paper,　a　fault-tolerant-based　online　critic　learning　algorithm　is　developed　to　solve　the　optimal　tracking　control　issue　for　nonaffine　nonlinear　systems　with　actuator　faults.　First,　a　novel　augmented　plant　is　constructed　by　fusing　the　system　state　and　the　reference　trajectory,　which　aims　to　transform　the　optimal　fault-tolerant　tracking　control　design　with　actuator　faults　into　the　optimal　regulation　problem　of　the　conventional　nonlinear　error　system.　Subsequently,　in　order　to　ensure　the　normal　execution　of　the　online　learning　algorithm,　a　stability　criterion　condition　is　created　to　obtain　an　initial　admissible　tracking　policy.　Then,　the　constructed　model　neural　network　(NN)　is　pre-trained　to　recognize　the　system　dynamics　and　calculate　trajectory　control.　The　critic　and　action　NNs　are　constructed　to　output　the　approximate　cost　function　and　approximate　tracking　control,　respectively.　The　Hamilton-Jacobi-Bellman　equation　of　the　error　system　is　solved　online　through　the　action-critic　framework.　In　theoretical　analysis,　it　is　proved　that　all　concerned　signals　are　uniformly　ultimately　bounded　according　to　the　Lyapunov　principle.　The　tracking　control　law　can　approach　the　optimal　tracking　control　within　a　finite　approximation　error.　Finally,　two　experimental　examples　are　conducted　to　indicate　the　effectiveness　and　supe-riority　of　the　developed　fault-tolerant　tracking　control　scheme.