This　paper　proposes　a　data-driven　dual-mode　model　predictive　control　method　for　linear　discrete　systems　with　unknown　parameters,　eliminating　the　need　for　system　modeling.　The　proposed　method　enables　optimal　tracking　of　the　setpoint　under　constraints.　First,　the　system＇s　future　trajectory　is　predicted　based　on　limited　historical　operational　data,　and　a　real-time　optimized　artificial　equilibrium　point　is　incorporated　into　the　cost　function.　Control　inputs　are　then　determined　by　solving　an　online　rolling　optimization　problem,　driving　the　system　into　a　control-invariant　set.　Within　this　invariant　set,　a　dynamic　feedback　controller　is　derived　using　the　policy　iteration　method　based　on　historical　operational　data,　and　a　static　feedforward　controller　is　also　obtained,　achieving　locally　optimal　control　in　guiding　the　system　to　converge　to　the　equilibrium　point.　Finally,　the　stability　of　the　proposed　method　is　proven,　and　it　is　applied　to　a　linearized　four-tank　system.　Experimental　results　demonstrate　the　effectiveness　and　feasibility　of　the　proposed　method,　with　reduced　overshoot　and　improved　convergence　performance.　©　2025　Northeast　University.　All　rights　reserved.