High rockfill embankment is widely applied to high-grade highways in mountainous areas, and its stable operation can significantly promote traffic and economic development. In this study, a stability evaluation model of high rockfill embankment was constructed based on fuzzy mathematics theory to realize effective and rapid evaluation. Considering representativeness and easy operability, 5 level-1 factor indexes and 16 level-2 factor indexes were determined from different perspectives of embankment fillers, slope conditions, hydraulic conditions, slope protection, and other factors. Weights of different factor indexes were allocated using analytic hierarchy process and all factor indexes were quantified and classified using the fuzzy statistical method according to stability level. Reliabilities of factor indexes were combined with reliability theory for analysis. Finally, the reasonability of the proposed fuzzy evaluation model was verified through application in the high rockfill embankment engineering on the secondary highway of the National Highway 316 from Xunyang to Ankang in Shaanxi Province, China. Results demonstrate that the reliability of this high rockfill embankment is 0.8671 and the final score of the evaluation system is 73.1619, indicating stability. Therefore, the proposed fuzzy evaluation model is reasonable and feasible to evaluate stability of high rockfill embankment, and can provide references for future design.

Evaluation of stability of high rockfill embankment based on fuzzy mathematics

A multi degree of freedom (DOF) robot is a complex and variable nonlinear system, and its control performance is affected by the inherent parameters of the model itself, friction, external disturbance, and other factors. A robust control method based on neural network disturbance observer was proposed in this study to improve the effect of time-varying system parameters and external disturbances on the control system performance. A new dynamic model of robot error was constructed by analyzing the characteristics of the robot system model. The total disturbance of the system was observed and compensated online on the basis of the neural network observer, and the effectiveness of the control method was verified through simulation. Results demonstrate that the robust adaptive control method with neural network disturbance observer reduces the maximum angular displacement error by 2.7 times and the maximum angular velocity tracking error by 2.14 times compared with the control method without observer when model parameter perturbation and external disturbance are found in the system. The maximum angular velocity error is 4 and 88.6 times lower than proportional derivative (PD) compensation control and traditional sliding mode control, respectively. The neural network disturbance observer can accurately track the total disturbance of the system. The input torque of the proposed control method has a small peak torque, which is 1/8 and 1/2 times lower than the sliding mode control and PD compensation control, respectively, and the control curve of the proposed control method is relatively smooth. The proposed method provides a reference for the multi DOF robot to achieve high-precision tracking in complex and changeable environment.

/pdf/robust-control-of-multi-degree-of-freedom-robot-based-on-2e0jufktdy.pdf

Jimeng Zhang

Papers

Robust Control of Multi Degree of Freedom Robot Based on Disturbance Observer of Neural Network