Existing　model　predictive　control　(MPC)　methods　lack　online　learning　capability　in　complex　environments.　Reinforcement　learning　(RL)　requires　a　lot　of　data　and　computing　resources　to　obtain　optimal　control.　This　brief　uses　efficient　self-learning　to　solve　this　problem.　＂Efficient＂　refers　to　the　use　of　triggered-learning　mechanism　(TLM)　to　manage　computing　resources　on　demand.　This　brief　proposes　a　triggered-learning　model　predictive　control　(TL-MPC)　method　for　switched　systems.　The　proposed　TL-MPC　endows　MPC　with　learning　capabilities　through　the　TLM.　TLM　includes　a　Deep　Deterministic　Policy　Gradient　(DDPG)　based　control　incremental　self-learning　tuning　strategy　and　a　performance-driven　event-triggering　strategy.　The　first　strategy　is　to　give　the　MPC　controller　a　control　increment　to　optimize　control　effect.　The　second　strategy　is　to　realize　the　on-demand　learning　and　reduce　computational　resources　by　comparing　two　cost　functions　that　characterize　the　system　performance.　In　addition,　the　stability　of　switched　systems　under　TL-MPC　is　analyzed　using　the　Lyapunov　function　and　the　average　dwell　time　technique.　Finally,　the　effectiveness　of　the　proposed　method　is　verified　by　simulation.