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...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 TakagiSugeno 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 LyapunovⅡtheorem 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.展开更多
基金supported by the Science and Technology Innovation 2030-“New Generation Artificial Intelligence”Major Project of China(Grant No.2021ZD0112300)the Innovative Research Group Project of the National Natural Science Foundation of China(Grant No.62021003)+1 种基金the National Natural Science Foundation of China(Grant No.62073006)the Natural Science Foundation of Beijing(Grant Nos.4212032 and4192009)。
文摘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 TakagiSugeno 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 LyapunovⅡtheorem 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.
