Automatic segment assembly,which increases boring efficiency and construction safety,is a trend in tunnel boring.However,the current situation still relies on manual operation and experience.Electro-hydraulic rotation...Automatic segment assembly,which increases boring efficiency and construction safety,is a trend in tunnel boring.However,the current situation still relies on manual operation and experience.Electro-hydraulic rotation systems are crucial in segment grasping,transporting,and assembly.This article presents the automation of rotation systems in segment assembly to improve motion smoothness and accuracy.As a result of strong nonlinearity and system complexity,parameter estimation is performed by using a noise reduction method based on multialgorithm fusion and the stochastic gradient deviation correction recursive least squares identification algorithm.Active disturbance rejection control(ADRC)is introduced into sliding mode control(SMC)to compensate for model uncertainty and disturbance.An improved cuckoo algorithm is used to optimize influential parameters in ADRC.Moreover,full-scale bench tests are conducted to verify the proposed system automation.Results indicate that the proposed method has a better displacement tracking performance and lower tracking error than the ADRC,SMC,and proportional-integral-derivative methods.Furthermore,such a procedure facilitates the success rate of complete segment assembly.展开更多
基金supported by the Natural Science Foundation of Zhejiang Province,China(Grant No.LD22E050003)the National Natural Science Foundation of China(Grant No.52222503).
文摘Automatic segment assembly,which increases boring efficiency and construction safety,is a trend in tunnel boring.However,the current situation still relies on manual operation and experience.Electro-hydraulic rotation systems are crucial in segment grasping,transporting,and assembly.This article presents the automation of rotation systems in segment assembly to improve motion smoothness and accuracy.As a result of strong nonlinearity and system complexity,parameter estimation is performed by using a noise reduction method based on multialgorithm fusion and the stochastic gradient deviation correction recursive least squares identification algorithm.Active disturbance rejection control(ADRC)is introduced into sliding mode control(SMC)to compensate for model uncertainty and disturbance.An improved cuckoo algorithm is used to optimize influential parameters in ADRC.Moreover,full-scale bench tests are conducted to verify the proposed system automation.Results indicate that the proposed method has a better displacement tracking performance and lower tracking error than the ADRC,SMC,and proportional-integral-derivative methods.Furthermore,such a procedure facilitates the success rate of complete segment assembly.
