Event-triggered fuzzy neural multivariable control for a municipal solid waste incineration process

Because of coupling, nonlinearity, and uncertainty in a municipal solid waste incineration (MSWI) process, a suitable multivariable controller is difficult to establish under strong disturbance. Additionally, the problems of reducing mechanical wear and energy consumption in the control process should also be considered. To solve these problems, an event-triggered fuzzy neural multivariable controller is proposed in this paper. First, the MSWI object model based on the multiinput multioutput Takagi-Sugeno fuzzy neural network is established using a data-driven method. Second, a fuzzy neural multivariable controller is designed to control the furnace temperature and flue gas oxygen content synchronously under external disturbance. Third, an event-triggered mechanism is constructed to update the control rate online while ensuring control effects. Then, the stability of the proposed control strategy is proven through the Lyapunov II theorem to guide its practical application. Finally, the effectiveness of the controller is verified using the real industrial data of an MSWI factory in Beijing, China. The experimental results show that the proposed control strategy greatly improves the control efficiency, reduces energy consumption by 66.23%, and improves the multivariable tracking control accuracy.