The harsh working environment of unmanned mining electric shovels(UMESs)and the considerable inertia changes during the excavation process in the front-end mechanism pose major challenges to excavation trajectory trac...The harsh working environment of unmanned mining electric shovels(UMESs)and the considerable inertia changes during the excavation process in the front-end mechanism pose major challenges to excavation trajectory tracking.In this study,an adaptive Hamilton-Jacobi inequality(HJI)-based robust control method for UMES excavation systems with uncertainty was proposed for trajectory tracking control in intelligent mining.First,the excavation system dynamic model was analyzed using the Lagrangian method,and an excavation resistance prediction model and a material quality prediction model were constructed.The optimal excavation trajectory was described.Then,the HJI theorem was used to design an adaptive controller based on the dynamic model of the UMES,and a generalized regression neural network was introduced to fit the interference term in the control object to ensure the convergence of the control system.Subsequently,a Lyapunov function was constructed to demonstrate the stability of the control system to ensure the reliability of the excavation system.Finally,the method proposed in this study was verified under two different working conditions involving a typical material surface and a real material surface.The numerical simulation results demonstrated that the planned position and velocity were effectively tracked in both working conditions.Furthermore,it maintains an improved tracking effect under different uncertain disturbances,thus verifying the feasibility and robustness of the control system designed in this study.展开更多
Traditional coal mining and utilisation patterns are severely detrimental to natural resources and environments and significantly impede safe,low-carbon,clean,and sustainable utilisation of coal resources.Based on the...Traditional coal mining and utilisation patterns are severely detrimental to natural resources and environments and significantly impede safe,low-carbon,clean,and sustainable utilisation of coal resources.Based on the idea of in situ fluidized coal mining that aims to transform solid coal into liquid or gas and transports the fluidized resources to the ground to ensure safe mining and low-carbon and clean utilisation,in this study,we report on a novel in situ unmanned automatic mining method.This includes a flexible,earthworm-like unmanned automatic mining machine(UAMM)and a coal mine layout for in situ fluidized coal mining suitable for the UAMM.The technological and economic advantages and the carbon emission reduction of the UAMM-based in situ fluidized mining in contrast to traditional mining technologies are evaluated as well.The development trends and possible challenges to this design are also discussed.It is estimated that the proposed method costs approximately 49%of traditional coal mining costs.The UAMM-based in situ fluidized mining and transformation method will reduce CO_(2)emissions by at least 94.9%compared to traditional coal mining and utilisation methods.The proposed approach is expected to achieve safe and environmentally friendly coal mining as well as lowcarbon and clean utilisation of coal.展开更多
基金supported by the Major Science and Technology Project of Shanxi Province,China(Grant No.20191101014)the National Natural Science Foundation of China(Grant No.52075068).
文摘The harsh working environment of unmanned mining electric shovels(UMESs)and the considerable inertia changes during the excavation process in the front-end mechanism pose major challenges to excavation trajectory tracking.In this study,an adaptive Hamilton-Jacobi inequality(HJI)-based robust control method for UMES excavation systems with uncertainty was proposed for trajectory tracking control in intelligent mining.First,the excavation system dynamic model was analyzed using the Lagrangian method,and an excavation resistance prediction model and a material quality prediction model were constructed.The optimal excavation trajectory was described.Then,the HJI theorem was used to design an adaptive controller based on the dynamic model of the UMES,and a generalized regression neural network was introduced to fit the interference term in the control object to ensure the convergence of the control system.Subsequently,a Lyapunov function was constructed to demonstrate the stability of the control system to ensure the reliability of the excavation system.Finally,the method proposed in this study was verified under two different working conditions involving a typical material surface and a real material surface.The numerical simulation results demonstrated that the planned position and velocity were effectively tracked in both working conditions.Furthermore,it maintains an improved tracking effect under different uncertain disturbances,thus verifying the feasibility and robustness of the control system designed in this study.
基金The authors gratefully acknowledge the financial support provided by the State Key Research Development Program of China(Grant Number 2016YFC0600705)the National Natural Science Foundation of China(Grant Numbers 51674251,51727807,and 51374213)+1 种基金the National Major Project for Science and Technology(Grant Number 2017ZX05049003-006)and the Innovation Teams of Ten-thousand Talents Program sponsored by the Ministry of Science and Technology of China(Grant Number 2016RA4067).
文摘Traditional coal mining and utilisation patterns are severely detrimental to natural resources and environments and significantly impede safe,low-carbon,clean,and sustainable utilisation of coal resources.Based on the idea of in situ fluidized coal mining that aims to transform solid coal into liquid or gas and transports the fluidized resources to the ground to ensure safe mining and low-carbon and clean utilisation,in this study,we report on a novel in situ unmanned automatic mining method.This includes a flexible,earthworm-like unmanned automatic mining machine(UAMM)and a coal mine layout for in situ fluidized coal mining suitable for the UAMM.The technological and economic advantages and the carbon emission reduction of the UAMM-based in situ fluidized mining in contrast to traditional mining technologies are evaluated as well.The development trends and possible challenges to this design are also discussed.It is estimated that the proposed method costs approximately 49%of traditional coal mining costs.The UAMM-based in situ fluidized mining and transformation method will reduce CO_(2)emissions by at least 94.9%compared to traditional coal mining and utilisation methods.The proposed approach is expected to achieve safe and environmentally friendly coal mining as well as lowcarbon and clean utilisation of coal.
