A finite element vibration model of a multiple wheel-rail system which consists of four wheels, one rail, and a series of sleepers is established to address the problem of rail corrugation in high-speed tracks. In the...A finite element vibration model of a multiple wheel-rail system which consists of four wheels, one rail, and a series of sleepers is established to address the problem of rail corrugation in high-speed tracks. In the model, the creep forces between the wheels and rail are considered to be saturated and equal to the normal contact forces times the friction coefficient. The oscillation of the rail is coupled with that of wheels in the action of the saturated creep forces. When the coupling is strong, self- excited oscillation of the wheel-rail system occurs. The self-excited vibration propensity of the model is analyzed using the complex eigenvalue method. Results show that there are strong propensities of unstable self-excited vibrations whose frequencies are less than 1,200 Hz under some conditions. Preventing wheels from slipping on rails is an effective method for suppressing rail corrugation in high-speed tracks.展开更多
Wheeled mobile robots(WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly.To overcome this drawback,this article presents a neura...Wheeled mobile robots(WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly.To overcome this drawback,this article presents a neural network(NN) based terminal sliding mode control(TSMC) for WMRs where an augmented ground friction model is reported by which the uncertain friction can be estimated and compensated according to the required performance.In contrast to the existing friction models,the developed augmented ground friction model corresponds to actual fact because not only the effects associated with the mobile platform velocity but also the slippage related to the wheel slip rate are concerned simultaneously.Besides,the presented control approach can combine the merits of both TSMC and radial basis function(RBF) neural networks techniques,thereby providing numerous excellent performances for the closed-loop system,such as finite time convergence and faster friction estimation property.Simulation results validate the proposed friction model and robustness of controller;these research results will improve the autonomy and intelligence of WMRs,particularly when the mobile platform suffers from the sophisticated unstructured environment.展开更多
基金supported by the National Natural Science Foundation of China(No.51275429)
文摘A finite element vibration model of a multiple wheel-rail system which consists of four wheels, one rail, and a series of sleepers is established to address the problem of rail corrugation in high-speed tracks. In the model, the creep forces between the wheels and rail are considered to be saturated and equal to the normal contact forces times the friction coefficient. The oscillation of the rail is coupled with that of wheels in the action of the saturated creep forces. When the coupling is strong, self- excited oscillation of the wheel-rail system occurs. The self-excited vibration propensity of the model is analyzed using the complex eigenvalue method. Results show that there are strong propensities of unstable self-excited vibrations whose frequencies are less than 1,200 Hz under some conditions. Preventing wheels from slipping on rails is an effective method for suppressing rail corrugation in high-speed tracks.
基金supported by the National Natural Science Foundation of China(61573078,61573147)the International S&T Cooperation Program of China(2014DFB70120)the State Key Laboratory of Robotics and System(SKLRS2015ZD06)
文摘Wheeled mobile robots(WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly.To overcome this drawback,this article presents a neural network(NN) based terminal sliding mode control(TSMC) for WMRs where an augmented ground friction model is reported by which the uncertain friction can be estimated and compensated according to the required performance.In contrast to the existing friction models,the developed augmented ground friction model corresponds to actual fact because not only the effects associated with the mobile platform velocity but also the slippage related to the wheel slip rate are concerned simultaneously.Besides,the presented control approach can combine the merits of both TSMC and radial basis function(RBF) neural networks techniques,thereby providing numerous excellent performances for the closed-loop system,such as finite time convergence and faster friction estimation property.Simulation results validate the proposed friction model and robustness of controller;these research results will improve the autonomy and intelligence of WMRs,particularly when the mobile platform suffers from the sophisticated unstructured environment.
