Cooperative Event-Triggered Fuzzy-Neural Multivariable Control With Multitask Learning for Municipal Solid Waste Incineration Process
Haixu Ding, Junfei Qiao, Weimin Huang, Tao Yu · IEEE Transactions on Industrial Informatics · 2023
Municipal solid waste incineration (MSWI) is an industrial process with multiple mechanism reactions, which has strong coupling and time-varying dynamics. It is extremely difficult to design a feasible multivariable controller for MSWI process due to the complex composition of municipal solid waste and fluctuation of calorific value. To solve these problems, a cooperative event-triggered fuzzy-neural multivariable controller with multitask learning (CETFNMC-MTL) is proposed to realize the adaptive multivariable control of MSWI process. First, a fuzzy-neural multivariable controller is established to control furnace temperature and oxygen content synchronously. Second, a dynamic self-organizing mechanism based on multitask learning is designed, which splits and merges neurons by calculating the dynamic time warping distance and cumulative contribution of neurons in the continuous time. Third, a cooperative event-triggered mechanism is introduced to improve controller update efficiency while reducing mechanical wear and computational burden. Then, the stability of parameters learning and structure self-organizing process is analyzed to guarantee the successful application of CETFNMC-MTL. Finally, the effectiveness of the controller is tested with process data from an MSWI plant in Beijing, China. The results show that the proposed CETFNMC-MTL has adaptive learning ability, while reducing energy consumption and improving control accuracy.