In real-time hybrid simulation（RTHS）, it is difficult if not impossible to completely erase the error in restoring force due to actuator response delay using existing displacement-based compensation methods. This pa...In real-time hybrid simulation（RTHS）, it is difficult if not impossible to completely erase the error in restoring force due to actuator response delay using existing displacement-based compensation methods. This paper proposes a new force correction method based on online discrete tangent stiffness estimation（online DTSE） to provide accurate online estimation of the instantaneous stiffness of the physical substructure. Following the discrete curve parameter recognition theory, the online DTSE method estimates the instantaneous stiffness mainly through adaptively building a fuzzy segment with the latest measurements, constructing several strict bounding lines of the segment and calculating the slope of the strict bounding lines, which significantly improves the calculation efficiency and accuracy for the instantaneous stiffness estimation. The results of both computational simulation and real-time hybrid simulation show that：（1） the online DTSE method has high calculation efficiency, of which the relatively short computation time will not interrupt RTHS; and（2） the online DTSE method provides better estimation for the instantaneous stiffness, compared with other existing estimation methods. Due to the quick and accurate estimation of instantaneous stiffness, the online DTSE method therefore provides a promising technique to correct restoring forces in RTHS.展开更多
The stiffness information of the grasped object at the initial contact stage can be effectively used to adjust the grasping force of the prosthetic hand,thereby preventing damage to the object.However,the object’s de...The stiffness information of the grasped object at the initial contact stage can be effectively used to adjust the grasping force of the prosthetic hand,thereby preventing damage to the object.However,the object’s deformation and contact force are often minimal during the initial stage and not easily obtained directly.Additionally,stiffness estimation methods for prosthetic hands often require contact sensors,which can easily lead to poor contact issues.To address the above issues,this paper proposes the model-based stiffness estimation of grasped objects for underactuated prosthetic hands without force sensors.First,the kinematic model is linearized at the contact points to achieve the estimation of the linkage angles in the underactuated prosthetic hand.Secondly,the motor parameters are estimated using the Kalman filter method,and the grasping force is obtained from the dynamic model of the underactuated prosthetic hand.Finally,the contact model of the prosthetic hand grasping an object is established,and an online stiffness estimation method based on the contact model for the grasped object is proposed using the iterative reweighted least squares method.Experimental results show that this method can estimate the stiffness of grasped objects within 250 ms without contact sensors.展开更多
Four-Wheel Independent Steering(4WIS)Vehicles can independently control the angle of each wheel,demonstrating superior trajectory tracking performance under normal conditions.However,on intermittent icy and snowy road...Four-Wheel Independent Steering(4WIS)Vehicles can independently control the angle of each wheel,demonstrating superior trajectory tracking performance under normal conditions.However,on intermittent icy and snowy roads,the presence of time-varying adhesion coefficients,time-varying cornering stiffness,and the irregularities due to ice and snow accumulation introduce multiple uncertainties into the steering system,significantly degrading the trajectory tracking performance of 4WIS vehicles.In response,this paper proposes a robust Tube Model Predictive Control(Tube-MPC)trajectory tracking control method for 4WIS.In this method,a Bi-directional Long Short-Term Memory neural network is established for online estimation of tire cornering stiffness under different road adhesion coefficients,providing accurate estimation of time-varying cornering stiffness for each wheel to mitigate the uncertainties of time-varying adhesion coefficients and cornering stiffness.Additionally,considering the road irregularities caused by snow accumulation on intermittent icy and snowy roads,a trajectory tracking controller that integrates Tube-MPC and robust Sliding Mode Control is proposed.The nominal MPC model,developed from the estimated tire cornering stiffness,utilizes the sliding surface and the optimal auxiliary control unit law for the tube is derived from the reaching law in Tube-MPC,aiming to minimize the trajectory tracking error while enhancing the controller’s robustness against road uncertainties.The experiments show that the proposed method outperforms the Tube-MPC algorithm in terms of trajectory accuracy and robustness.This method demonstrates excellent trajectory tracking accuracy under intermittent icy and snowy road conditions,and it lays a theoretical foundation for future studies on vehicle stability and trajectory tracking under such road conditions.展开更多
To the editor,We read with interest the recent article by Li et al[1],published in Intelligent Medicine,which proposes a machine learning model to estimate corneal stiffness indicators and detect keratoconus without r...To the editor,We read with interest the recent article by Li et al[1],published in Intelligent Medicine,which proposes a machine learning model to estimate corneal stiffness indicators and detect keratoconus without relying on biomechanical equipment.The use of ensemble models—combining random forests with other classifiers—has demonstrated improved predictive performance,yielding a higher area under the receiver operating characteristic curve(AUC),an enhanced combined corneal biomechanical index(cCBI),superior intraclass correlation coefficient(ICC)values,and more accurate stiffness parameter at first applanation(SP-A1)measurements.(e.g.,ICC=0.82 for SP-A1 and AUC=0.935 for cCBI classification).These results support the idea of using surrogate modeling to democratize access to biomechanical diagnostics.Although we appreciate the study’s clinical and computational strengths,we believe that it is crucial to examine the mathematical foundations and fairness implications that underpin such systems.展开更多
基金Priority Academic Program Development of Jiangsu Higher Education Institutions under Grant No.1105007002National Natural Science Foundation of China under Grant No.51378107 and No.51678147
文摘In real-time hybrid simulation（RTHS）, it is difficult if not impossible to completely erase the error in restoring force due to actuator response delay using existing displacement-based compensation methods. This paper proposes a new force correction method based on online discrete tangent stiffness estimation（online DTSE） to provide accurate online estimation of the instantaneous stiffness of the physical substructure. Following the discrete curve parameter recognition theory, the online DTSE method estimates the instantaneous stiffness mainly through adaptively building a fuzzy segment with the latest measurements, constructing several strict bounding lines of the segment and calculating the slope of the strict bounding lines, which significantly improves the calculation efficiency and accuracy for the instantaneous stiffness estimation. The results of both computational simulation and real-time hybrid simulation show that：（1） the online DTSE method has high calculation efficiency, of which the relatively short computation time will not interrupt RTHS; and（2） the online DTSE method provides better estimation for the instantaneous stiffness, compared with other existing estimation methods. Due to the quick and accurate estimation of instantaneous stiffness, the online DTSE method therefore provides a promising technique to correct restoring forces in RTHS.
基金supported by the National Natural Science Foundation of China under Grant 52275297.
文摘The stiffness information of the grasped object at the initial contact stage can be effectively used to adjust the grasping force of the prosthetic hand,thereby preventing damage to the object.However,the object’s deformation and contact force are often minimal during the initial stage and not easily obtained directly.Additionally,stiffness estimation methods for prosthetic hands often require contact sensors,which can easily lead to poor contact issues.To address the above issues,this paper proposes the model-based stiffness estimation of grasped objects for underactuated prosthetic hands without force sensors.First,the kinematic model is linearized at the contact points to achieve the estimation of the linkage angles in the underactuated prosthetic hand.Secondly,the motor parameters are estimated using the Kalman filter method,and the grasping force is obtained from the dynamic model of the underactuated prosthetic hand.Finally,the contact model of the prosthetic hand grasping an object is established,and an online stiffness estimation method based on the contact model for the grasped object is proposed using the iterative reweighted least squares method.Experimental results show that this method can estimate the stiffness of grasped objects within 250 ms without contact sensors.
基金Supported by National Natural Science Foundation of China(Grant Nos.52405112,U24A20199)the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20240973).
文摘Four-Wheel Independent Steering(4WIS)Vehicles can independently control the angle of each wheel,demonstrating superior trajectory tracking performance under normal conditions.However,on intermittent icy and snowy roads,the presence of time-varying adhesion coefficients,time-varying cornering stiffness,and the irregularities due to ice and snow accumulation introduce multiple uncertainties into the steering system,significantly degrading the trajectory tracking performance of 4WIS vehicles.In response,this paper proposes a robust Tube Model Predictive Control(Tube-MPC)trajectory tracking control method for 4WIS.In this method,a Bi-directional Long Short-Term Memory neural network is established for online estimation of tire cornering stiffness under different road adhesion coefficients,providing accurate estimation of time-varying cornering stiffness for each wheel to mitigate the uncertainties of time-varying adhesion coefficients and cornering stiffness.Additionally,considering the road irregularities caused by snow accumulation on intermittent icy and snowy roads,a trajectory tracking controller that integrates Tube-MPC and robust Sliding Mode Control is proposed.The nominal MPC model,developed from the estimated tire cornering stiffness,utilizes the sliding surface and the optimal auxiliary control unit law for the tube is derived from the reaching law in Tube-MPC,aiming to minimize the trajectory tracking error while enhancing the controller’s robustness against road uncertainties.The experiments show that the proposed method outperforms the Tube-MPC algorithm in terms of trajectory accuracy and robustness.This method demonstrates excellent trajectory tracking accuracy under intermittent icy and snowy road conditions,and it lays a theoretical foundation for future studies on vehicle stability and trajectory tracking under such road conditions.
文摘To the editor,We read with interest the recent article by Li et al[1],published in Intelligent Medicine,which proposes a machine learning model to estimate corneal stiffness indicators and detect keratoconus without relying on biomechanical equipment.The use of ensemble models—combining random forests with other classifiers—has demonstrated improved predictive performance,yielding a higher area under the receiver operating characteristic curve(AUC),an enhanced combined corneal biomechanical index(cCBI),superior intraclass correlation coefficient(ICC)values,and more accurate stiffness parameter at first applanation(SP-A1)measurements.(e.g.,ICC=0.82 for SP-A1 and AUC=0.935 for cCBI classification).These results support the idea of using surrogate modeling to democratize access to biomechanical diagnostics.Although we appreciate the study’s clinical and computational strengths,we believe that it is crucial to examine the mathematical foundations and fairness implications that underpin such systems.
