This　paper　explores　online　learning　model　predictive　control　(MPC)　for　switched　systems　with　stable　terminal　constraints.　In　order　to　reduce　the　computational　burden,　we　decompose　the　infinite　horizon　MPC　into　n　finite　horizons,　and　adopt　adaptive　dynamic　programming　(ADP)　to　assist　MPC　in　online　solving　the　optimal　control　problem　in　each　finite　horizon.　Further,　we　use　a　set　of　stable　terminal　constraints　to　ensure　both　the　convergence　of　online　learning　MPC　in　each　finite　horizon　and　the　connection　of　adjacent　finite　horizons.　In　addition,　in　order　to　ensure　the　uniform　ultimate　boundedness　(UUB)　of　the　triggered　switched　systems,　on　the　one　hand,　a　novel　performance　dependent　mixed　switching　law　(PD-MSL)　is　proposed　to　both　avoid　frequent　switching　and　take　advantage　of　performance　dependent　decision-making;　on　the　other　hand,　an　analytical　framework　of　the　coupling　between　mode　switching　and　event-triggering　is　proposed.　Finally,　the　validity　of　the　presented　approach　is　demonstrated　through　simulations.　©　2024　The　Franklin　Institute