In　this　paper,　an　adaptive　critic　method　based　on　neural　networks　is　established　to　solve　the　tracking　con-trol　problem　for　multi-person　zero-sum　games　with　constrained　nonlinear　dynamics.　First,　an　augmented　system　is　constructed　with　the　tracking　error　system　and　the　reference　system,　an　appropriate　function　is　introduced　to　handle　the　constrained　problem,　and　a　constrained　tracking　Hamilton-Jacobi-Isaacs　(HJI)　equation　is　derived　for　the　augmented　system.　Then,　a　constrained　tracking　design　with　neural　critic　learning　for　multi-person　zero-sum　games　is　developed　to　approximately　solve　the　tracking　HJI　equation　with　input　constraints.　A　new　updating　rule　is　given　and　only　one　critic　network　is　employed　during　neural　critic　learning.　In　addition,　we　prove　that　the　tracking　error　in　the　augmented　system　is　uniformly　ulti-mately　bounded　by　using　Lyapunov＇s　direct　method.　Finally,　an　example　is　given　to　verify　the　effectiveness　of　the　proposed　method.　In　this　example,　we　make　the　number　of　control　inputs　less　than　the　number　of　disturbance　inputs.　(C)　2022　Elsevier　B.V.　All　rights　reserved.