Spiral bevel gears are critical transmission components,and are widely used in the aerospace field.This paper proposes a new multi-DOF envelope forming process for fabricating spiral bevel gears.Firstly,the multi-DOF ...Spiral bevel gears are critical transmission components,and are widely used in the aerospace field.This paper proposes a new multi-DOF envelope forming process for fabricating spiral bevel gears.Firstly,the multi-DOF envelope forming principle of spiral bevel gears is proposed.Secondly,the design methods for the envelope tool geometry and movement are proposed based on the envelope geometry and movement relationships.Thirdly,the metal flow and tooth filling laws are revealed through 3D FE simulation of the multi-DOF envelope forming process of a typical spiral bevel gear.Fourthly,a new method for separating the envelope tool and the formed spiral bevel gear with back taper tooth is proposed to avoid their interference.Finally,experiments on multi-DOF envelope forming of this typical spiral bevel gear are conducted using new heavy load multi-DOF envelope forming equipment.The simulation and experimental results show the feasibility of the proposed multi-DOF envelope forming process for fabricating spiral bevel gears with back taper tooth and the corresponding process design methods.展开更多
This paper mainly presents a PM multi-DOF actuator for robot in-wheels drive applications and its control method. The torque model is established based on the finite-element model of the single pair poles in 3D finite...This paper mainly presents a PM multi-DOF actuator for robot in-wheels drive applications and its control method. The torque model is established based on the finite-element model of the single pair poles in 3D finite element method software. Due to the special structure of the actuator,the Euler angles are adopted for deriving the kinematics and dynamic model. To reduce the effects of uncertainties of modeling error,nonlinear friction and external disturbances in the system,an approximation of neural network adaptive control method is applied to the actuator. The computation and simulation results show that the proposed analysis and control method can effectively derive the characteristics and improve the motion performance,which provides the primary theoretical guide for the configuration design,optimization and control research of multi-DOF deflection type actuators.展开更多
To achieve the manufacturing of Thin-Wall and High-Rib Components(TWHRC)with high precision,a novel heavy load Multi-DOF Envelope Forming Press(MEFP)with Parallel Kinematic Mechanism(PKM),driven by six Permanent Magne...To achieve the manufacturing of Thin-Wall and High-Rib Components(TWHRC)with high precision,a novel heavy load Multi-DOF Envelope Forming Press(MEFP)with Parallel Kinematic Mechanism(PKM),driven by six Permanent Magnet Synchronous Motors(PMSMs),is developed.However,on account of the heavy forming load,the PMSM parameters are in great variation.Meanwhile,the PMSM is always in a transient state caused by fast time-varying forming load,resulting in low identification precision of varied PMSM parameters and control precision of PMSM under traditional parameter identification methods.To solve this problem,a novel Sliding Mode Control Method with Enhanced PMSM Parameter Identification(SMCMEPPI)for heavy load MEFP is proposed.Firstly,the kinematic model of MEFP is established.Secondly,the variation law of PMSM parameters under heavy load is revealed.Thirdly,an enhanced PMSM parameter identification method is proposed,in which the q axis current of PMSM is used to represent the changing rate of forming load and the adjustment factor is first proposed to remove improper input of PMSM parameter identification online.Fourthly,the Electromechanical Coupling Dynamic Model(ECDM)of MEFP,which includes identified PMSM parameters,is developed.Finally,based on the developed ECDM,a novel SMCMEPPI is proposed to realize the high-precision control of heavy load MEFP.The experimental results indicate that the proposed SMCMEPPI can significantly improve the control precision of heavy load MEFP.展开更多
Reconstructing limb function represents a shared goal between researchers and amputees.However,the development of human-machine interfaces for decoding multi-degree-of-freedom(multi-DoF)movements remains challenging d...Reconstructing limb function represents a shared goal between researchers and amputees.However,the development of human-machine interfaces for decoding multi-degree-of-freedom(multi-DoF)movements remains challenging due to muscle crosstalk,co-activation,and incomplete extraction of motor unit(MU)activities in surface electromyography(sEMG)signals.To address these issues,this study proposes an enhanced neural-driven musculoskeletal model(MM)by integrating MU classification into the decoding process.Six sequential two-DoF movement tasks were designed and a classification framework containing eight task-specific separation matrices was established based on the selective activation of the MUs.The interference between multi-DoF movements was significantly reduced by refining the separation matrices,which effectively removed the MUs co-activated by multiple DoFs.The refined separation matrices were used to derive neural drives,which were subsequently integrated into the proposed four-DoF MM,and the accuracy loss resulting from reduced MU counts was compensated through the iterative optimization of physiological parameters.The proposed method was evaluated by an offline experiment involving 13 participants,and then compared with both classical neural-driven and non-negative matrix factorization(NMF)-driven MMs.Results demonstrated significant improvements in both correlation coefficient and normalized root mean square error,especially in complex four-DoF movement tasks.This study offers a novel and biologically grounded decoding strategy that enhances multi-DoF movement prediction and provides a promising direction for advanced prosthetic control.展开更多
基金the National Science and Technology Major Project of China(No.2019-VII0017e0158)the National Natural Science Foundation of China(No.U21A20131)+1 种基金the Industry-University Research Cooperation Project,China(No.HFZL2020CXY025)the National Key Laboratory of Science and Technology on Helicopter Transmission,China(No.HTL-O-21G05).
文摘Spiral bevel gears are critical transmission components,and are widely used in the aerospace field.This paper proposes a new multi-DOF envelope forming process for fabricating spiral bevel gears.Firstly,the multi-DOF envelope forming principle of spiral bevel gears is proposed.Secondly,the design methods for the envelope tool geometry and movement are proposed based on the envelope geometry and movement relationships.Thirdly,the metal flow and tooth filling laws are revealed through 3D FE simulation of the multi-DOF envelope forming process of a typical spiral bevel gear.Fourthly,a new method for separating the envelope tool and the formed spiral bevel gear with back taper tooth is proposed to avoid their interference.Finally,experiments on multi-DOF envelope forming of this typical spiral bevel gear are conducted using new heavy load multi-DOF envelope forming equipment.The simulation and experimental results show the feasibility of the proposed multi-DOF envelope forming process for fabricating spiral bevel gears with back taper tooth and the corresponding process design methods.
基金Sponsored by the National Natural Science Foundation of China(Grant No.51107031,50677013)the Natural Science Foundation of Hebei Province of China(Grant No.E2009000703)
文摘This paper mainly presents a PM multi-DOF actuator for robot in-wheels drive applications and its control method. The torque model is established based on the finite-element model of the single pair poles in 3D finite element method software. Due to the special structure of the actuator,the Euler angles are adopted for deriving the kinematics and dynamic model. To reduce the effects of uncertainties of modeling error,nonlinear friction and external disturbances in the system,an approximation of neural network adaptive control method is applied to the actuator. The computation and simulation results show that the proposed analysis and control method can effectively derive the characteristics and improve the motion performance,which provides the primary theoretical guide for the configuration design,optimization and control research of multi-DOF deflection type actuators.
基金the National Science and Technology Major Project of China(No.2019-Ⅶ-0017-0158)the National Natural Science Foundation of China(Nos.U2037204,U21A20131)the Innovative Research Team Development Program of Ministry of Education of China(No.IRT17R83)for the support given to this research。
文摘To achieve the manufacturing of Thin-Wall and High-Rib Components(TWHRC)with high precision,a novel heavy load Multi-DOF Envelope Forming Press(MEFP)with Parallel Kinematic Mechanism(PKM),driven by six Permanent Magnet Synchronous Motors(PMSMs),is developed.However,on account of the heavy forming load,the PMSM parameters are in great variation.Meanwhile,the PMSM is always in a transient state caused by fast time-varying forming load,resulting in low identification precision of varied PMSM parameters and control precision of PMSM under traditional parameter identification methods.To solve this problem,a novel Sliding Mode Control Method with Enhanced PMSM Parameter Identification(SMCMEPPI)for heavy load MEFP is proposed.Firstly,the kinematic model of MEFP is established.Secondly,the variation law of PMSM parameters under heavy load is revealed.Thirdly,an enhanced PMSM parameter identification method is proposed,in which the q axis current of PMSM is used to represent the changing rate of forming load and the adjustment factor is first proposed to remove improper input of PMSM parameter identification online.Fourthly,the Electromechanical Coupling Dynamic Model(ECDM)of MEFP,which includes identified PMSM parameters,is developed.Finally,based on the developed ECDM,a novel SMCMEPPI is proposed to realize the high-precision control of heavy load MEFP.The experimental results indicate that the proposed SMCMEPPI can significantly improve the control precision of heavy load MEFP.
基金supported by the National Key R&D Program of China(Grant No.2022YFB4700801)the National Natural Science Foundation of China(Grant No.52525504)the Emerging Frontiers Cultivation Program of Tianjin University Interdisciplinary Center。
文摘Reconstructing limb function represents a shared goal between researchers and amputees.However,the development of human-machine interfaces for decoding multi-degree-of-freedom(multi-DoF)movements remains challenging due to muscle crosstalk,co-activation,and incomplete extraction of motor unit(MU)activities in surface electromyography(sEMG)signals.To address these issues,this study proposes an enhanced neural-driven musculoskeletal model(MM)by integrating MU classification into the decoding process.Six sequential two-DoF movement tasks were designed and a classification framework containing eight task-specific separation matrices was established based on the selective activation of the MUs.The interference between multi-DoF movements was significantly reduced by refining the separation matrices,which effectively removed the MUs co-activated by multiple DoFs.The refined separation matrices were used to derive neural drives,which were subsequently integrated into the proposed four-DoF MM,and the accuracy loss resulting from reduced MU counts was compensated through the iterative optimization of physiological parameters.The proposed method was evaluated by an offline experiment involving 13 participants,and then compared with both classical neural-driven and non-negative matrix factorization(NMF)-driven MMs.Results demonstrated significant improvements in both correlation coefficient and normalized root mean square error,especially in complex four-DoF movement tasks.This study offers a novel and biologically grounded decoding strategy that enhances multi-DoF movement prediction and provides a promising direction for advanced prosthetic control.
