In　recent　years,　model　predictive　control　(MPC)　is　widely　utilized　to　address　the　tracking　problem　of　the　practical　industrial　processes.　In　this　paper,　in　terms　of　the　advantages　of　adaptive　dynamic　programming　(ADP),　the　adaptive　critic　trajectory　tracking　predictive　control　(ACTTPC)　framework　is　designed　to　tackle　tracking　predictive　control　problems　for　unknown　nonaffine　systems.　First,　the　unknown　system　dynamics　are　approximated　by　the　established　model　network.　Meanwhile,　the　feedforward　steady　control　is　considered　to　assist　with　accomplishing　the　tracking　mission.　Further,　in　each　prediction　horizon,　the　adaptive　critic　learning　method　is　utilized　to　solve　the　open-loop　optimization　problem　satisfying　some　conditions.　Afterwards,　the　Lyapunov　stability　of　the　augmented　error　system　is　fully　proved,　and　the　convergence　of　the　ACTTPC　algorithm　is　analyzed　in　detail.　Finally,　a　nonaffine　system　and　a　torsional　pendulum　plant　are　applied　to　validate　the　effectiveness　of　the　presented　approach　in　solving　the　tracking　problems.　Note　to　Practitioners-Many　industrial　processes　are　nonlinear　nonaffine　systems,　which　causes　a　great　challenge　to　solve　Hamilton-Jacobi-Bellman　(HJB)　equations　for　nonlinear　MPC　(NMPC).　Therefore,　it　is　quite　valuable　to　solve　the　NMPC　problem　by　using　the　advantages　of　ADP　in　addressing　nonlinear　HJB　equations.　In　this　paper,　the　ACTTPC　algorithm　is　designed　to　guide　the　trajectory　tracking　predictive　control　for　unknown　system　dynamics　in　the　practical　industrial　processes.　Generally　speaking,　the　mathematical　model　of　complex　industrial　systems　is　difficultly　established.　Hence,　the　model　network　is　built　via　selecting　a　batch　of　data　and　it　is　seen　as　the　prediction　model.　The　introduced　feedforward　steady　control　can　not　only　assist　realizing　trajectory　tracking　but　also　maintain　stable　tracking　effect.　Meanwhile,　the　feedback　predictive　control　input　is　solved　via　the　ACTTPC　algorithm.　The　simulation　experiments　are　conducted　to　prove　the　effectiveness　of　the　presented　algorithm.　Moreover,　the　pseudo-code　and　the　relevant　experimental　parameters　are　given.　For　different　systems　and　reference　trajectories,　the　practitioners　can　realize　tracking　tasks　via　modulating　the　related　parameters.