In　this　article,　in　order　to　achieve　optimal　tracking　control　of　unknown　linear　discrete　sys-tems,　a　model-free　scheme　based　on　Q-learning　is　established　online.　First,　we　introduce　an　innovative　performance　index　function,　so　as　to　eliminate　the　tracking　error　and　avert　the　calculation　for　stable　control　policies　of　the　reference　trajectory.　Taking　value　iteration　and　policy　iteration　into　consideration,　the　corresponding　model-based　approaches　are　derived.　Then,　the　Q-function　is　developed　and　the　model-free　algorithm　utilizing　Q-learning　is　given　for　the　sake　of　dealing　with　the　linear　quadratic　tracking　(LQT)　problem　online　with-out　relying　on　system　dynamics　information.　In　addition,　novel　stability　analysis　based　on　Q-learning　is　provided　for　the　discounted　LQT　control　issue　and　the　probing　noise　is　demonstrated　that　it　does　not　result　in　any　excitation　noise　bias.　Finally,　by　means　of　con-ducting　numerical　simulation,　the　proposed　Q-learning　algorithm　is　demonstrated　to　be　effective　and　practicable.(c)　2023　Elsevier　Inc.　All　rights　reserved.