In　practical　applications,　sampled-data　systems　are　often　affected　by　unforeseen　physical　constraints　that　may　cause　deviations　in　the　sampling　interval　from　the　expected　value　and　result　in　fluctuations　in　a　probabilistic　way,　where　the　probability　distribution　of　stochastic　sampling　intervals　is　often　time-varying　and　unknown.　How　to　design　a　stable　tracking　controller　for　sampled-data　control　systems　affected　by　unknown　stochastic　sampling　probability　(USSP)　is　a　challenging　task.　A　stochastic　sampled-data　model　predictive　control　(SSDMPC)　strategy　for　T-S　fuzzy　systems　(TSFSs)　is　proposed　to　overcome　this　challenge.　First,　based　on　the　input　delay　approach,　the　considered　system　is　modeled　as　a　continuous-time　TSFS　with　stochastic　input　delay.　Then,　the　stochastic　nature　of　the　sampling　interval　is　effectively　mapped　to　the　input　delay　within　the　TSFS.　Second,　considering　the　unknown　characteristic　of　the　sampling　interval,　a　Q-learning-based　online　estimation　algorithm　is　developed　to　acquire　the　sampling　probability,　and　an　event-triggered　mechanism　is　designed　to　reduce　the　computational　burden　of　the　estimation　algorithm.　Furthermore,　the　mapped　stochastic　input　delay　probability　can　be　obtained.　Third,　to　achieve　stable　tracking　control　of　the　above-mentioned　continuous-time　TSFS　with　stochastic　input　delay,　a　predictive　controller　is　designed　to　obtain　the　control　law.　Finally,　the　stability　of　SSDMPC　is　analyzed　theoretically　to　ensure　its　reliability.　Additionally,　the　effectiveness　of　SSDMPC　is　confirmed　through　numerical　simulations.　IEEE