This　article　studies　learning　empirical　inherited　intelligent　model　predictive　control　(LEII-MPC)　for　switched　systems.　For　complex　environments　and　systems,　an　intelligent　control　method　design　with　learning　ability　is　necessary　and　meaningful.　First,　a　switching　law　that　coordinates　the　iterative　learning　control　action　is　devised　according　to　the　average　dwell　time　approach.　Second,　an　intelligent　MPC　mechanism　with　the　iteration　learning　experience　is　designed　to　optimize　the　control　action.　With　the　designed　LEII-MPC,　sufficient　conditions　for　the　switched　systems　stability　equipped　with　the　event-triggering　schemes　(ETSs)　in　both　the　time　domain　and　the　iterative　domain　are　presented.　The　ETS　in　the　iterative　domain　is　to　solve　unnecessary　iterative　updates.　The　ETS　in　the　time　domain　is　to　deal　with　potential　denial　of　service　(DoS)　attacks,　which　includes　two　parts:　1)　for　detection,　an　attack-dependent　event-triggering　method　is　presented　to　determine　attack　sequence　and　reduce　lost　packets;　and　2)　for　compensation,　a　buffer　is　used　to　ensure　system　performance　during　the　attack　period.　Last,　a　numerical　example　shows　the　effectiveness　of　the　proposed　method.　©　2020　IEEE.