A　neural　controller　based　on　continuous　Hopfield　neural　network　(CHNN)　is　developed　to　solve　the　dynamic　tracking　optimal　control　problem　for　linear,　time-variant,　discrete-time　and　multivariabie　systems.　In　this　study,　CHNN　is　designed　to　perform　the　function　of　an　optimal　controller.　The　CHNN　is　constructed　by　establishing　the　equivalence　between　linear　quadratic　(LQ)　optimal　performance　index　of　control　system　and　the　energy　function　of　CHNN.　Stability　of　the　CHNN　is　analyzed　from　a　theoretical　perspective,　too.　As　a　result,　solving　LQ　dynamic　tracking　optimal　problem　is　equivalent　to　operating　associated　Hopfield　network　from　its　initial　state　to　the　terminal　state　that　represents　the　optimal　control　sequence.　In　order　to　extend　optimal　control　from　finite　time　horizon　into　infinite　time　horizon　and　realize　closed　loop　control,　an　online　rolling　optimization　algorithm　is　applied.　Numerical　simulation　shows　that　the　design　method　above　is　correct　and　feasible.