Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process

Due to the large uncertainty in the municipal solid waste incineration (MSWI) process, the furnace temperature of the MSWI process is difficult to control and the controller is updated frequently. To improve the accuracy and reduce the number of controller updates, a novel event-triggered control method based correntropy self-organizing TS fuzzy neural network (ET-CSTSFNN) is proposed. Firstly, the neurons of the rule layer are grown or pruned adaptively based on activation intensity and control error to meet the dynamic change of the actual operating condition. Meanwhile, the performance index is designed based on the correntropy of tracking errors, and the parameters of the controller are adjusted by gradient descent algorithm. Secondly, a fixed threshold event-triggered condition is designed to determine whether the current controller is updated or not. The stability of the control system is proved based on the Lyapunov stability theory. Finally, the furnace temperature control experiments are conducted based on the actual data of a municipal solid waste incineration plant in Beijing. The experimental results show that the proposed ET-CSTSFNN controller shows a better control performance, which can reduce the number of the controller update significantly while achieving accurate furnace temperature control compared with other traditional control methods.

Automated summary

Due to the large uncertainty in the MSWI process, a novel event-triggered control method based correntropy self-organizing TS fuzzy neural network (ET-CSTSFNN) is proposed. The method adaptively grows or prunes the neurons in the rule layer based on activation intensity and control error. The proposed ET-CSTSFNN controller achieves accurate furnace temperature control and reduces the number of controller updates significantly compared with other traditional control methods.