The　study　of　aperiodic　sampling　has　witnessed　enormous　interest　due　to　the　ubiquitous　presence　of　digital　controllers　in　relevant　application　domains.　Most　existing　aperiodic　sampled-data　control　methods　assume　that　the　model　of　the　system　is　known　or　obtainable,　though　it　is　unknown　and　unavailable　in　reality.　The　idealized　assumption　will　limit　the　application　of　these　methods.　To　address　this　problem,　a　data-driven　model　predictive　control　(DMPC)　strategy　is　designed　to　stabilize　the　aperiodic　sampled-data　unknown　nonlinear　systems　(ASUNSs).　The　main　contributions　of　the　proposed　DMPC　are　threefold.　First,　a　linearized　polytopic　approximation　dynamic　(LPAD),　based　on　the　local　linear　approximation,　is　constructed　to　approximate　the　dynamics　of　ASUNSs.　Then,　the　aperiodic　sampling　information　of　ASUNS　is　able　to　be　contained.　Second,　a　data-driven　model　predictive　controller　is　designed　to　solve　the　optimal　steady-state　problem　and　the　optimal　control　problem　(OCP)　successively.　Then,　the　desired　output　reference　can　be　tracked　online.　Third,　the　stability　of　DMPC　is　analyzed　in　theory.　Then,　the　corresponding　stability　conditions　are　given　to　ensure　its　successful　applications.　Finally,　some　experimental　studies　have　been　performed　on　the　online　control　of　a　general　unknown　nonlinear　system　(GUNS)　with　aperiodic　sampling　and　wastewater　treatment　process　(WWTP)　with　aperiodic　sampling　to　verify　its　effectiveness.