Interval Type-II Fuzzy Broad Model Predictive Control Based on the Static and Dynamic Hybrid Event-Triggering Mechanism and Adaptive Compensation for Furnace Temperature in the MSWI Process

Municipal solid waste incineration (MSWI) plays a key role in advancing environmental sustainability. However, the current main furnace temperature control methods are difficult to solve the problems of strong coupling, equipment wear, and frequent disturbances. To solve the above problems, in this article, we propose a static and dynamic hybrid event-triggering mechanism-based interval type-II fuzzy broad adaptive compensation model predictive control (SDHETM-IT2FB-ACMPC). Firstly, a furnace temperature prediction model based on the interval type-2 fuzzy broad learning system (IT2FBLS) is constructed, and the IT2FB-MPC method is obtained, which solve the problem of variable coupling. Secondly, DETM based on historical error information is designed using sliding window method and combined with SETM to form SDHETM to drive the update of control variable to reduce the problem of equipment wear. Finally, the adaptive compensation control law of the adaptive compensation optimization control (ACOC) algorithm can compensate for the influence of the disturbance and the event-triggered mechanism on the control effect, and overcome the problem of frequent disturbances. Experimental results show that the proposed method reduces ISE to 0.2821, IAE to 0.1930, and DEVmax to 6.6269—reductions of 79%, 59%, and 8% compared to traditional NMPC—while cutting control actions by 71%. The results prove that IT2FB-MPC has excellent control performance for furnace temperature, and that SDHETM and ACOC can effectively reduce the triggering times and effectively compensate for the influence caused by disturbances and the lack of control variable updates. The proposed method successfully solves the control difficulties of furnace temperature in the MSWI process.