In order to find a feasible way to control excavator’s arm and realize autonomous excavation, the dynamic model for the boom of excavator’s arm which was regarded as a planar manipulator with three degrees of freedo...In order to find a feasible way to control excavator’s arm and realize autonomous excavation, the dynamic model for the boom of excavator’s arm which was regarded as a planar manipulator with three degrees of freedom was constructed with Lagrange equation. The excavator was retrofitted with electrohydraulic proportional valves, associated sensors (three inclinometers) and a computer control system (the motion controller of EPEC). The full nonlinear mathematic model of electrohydraulic proportional system was achieved. A discontinuous projection based on an adaptive robust controller to approximate the nonlinear gain coefficient of the valve was presented to deal with the nonlinearity of the whole system, the error was dealt with by robust feedback and an adaptive robust controller was designed. The experiment results of the boom motion control show that, using the controller, good performance for tracking can be achieved, and the peak tracking error of boom angles is less than 4°.展开更多
According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are comput...According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.展开更多
This work deals with analysis of dynamic behaviour of hydraulic excavator on the basis of developed dynamic-mathematical model.The mathematical model with maximum five degrees of freedom is extended by new generalized...This work deals with analysis of dynamic behaviour of hydraulic excavator on the basis of developed dynamic-mathematical model.The mathematical model with maximum five degrees of freedom is extended by new generalized coordinate which represents rotation around transversal main central axis of inertia of undercarriage.The excavator is described by a system of six nonlinear,nonhomogenous differential equations of the second kind.Numerical analysis of the differential equations has been done for BTH-600 hydraulic excavator with moving mechanism with pneumatic wheels.展开更多
In order to measure the backhoe vibratory excavating resistance of a hydraulic excavator fast and precisely,the influences of vibratory excavating depth,angle,vibratory frequency,amplitude,bucket inserting velocity an...In order to measure the backhoe vibratory excavating resistance of a hydraulic excavator fast and precisely,the influences of vibratory excavating depth,angle,vibratory frequency,amplitude,bucket inserting velocity and soil type on the vibratory excavating resistance were analyzed.Simulation analysis was carded out to establish the bucket inserting velocity,amplitude and vibratory frequency considered as secondary variables and excavating resistance as primary variable.A fttzzy membership function was introduced to improve the anti-noise capacity of support vector machine,which is a soft-sensing model on the hydraulic excavator＇s backhoe vibratory excavating resistance based on fuzzy support vector machine.The simulation result reveals that its maximum relative training and testing error are nearly 0.68% and-0.47%,respectively.It is concluded that the model has quite high modeling precision and generalization capacity,and it can measure the vibratory excavating resistance accurately,reliably and fast in an indirect way.展开更多
基金Project(2003AA430200) supported by the National Hi-Tech Research and Development Program(863) of China
文摘In order to find a feasible way to control excavator’s arm and realize autonomous excavation, the dynamic model for the boom of excavator’s arm which was regarded as a planar manipulator with three degrees of freedom was constructed with Lagrange equation. The excavator was retrofitted with electrohydraulic proportional valves, associated sensors (three inclinometers) and a computer control system (the motion controller of EPEC). The full nonlinear mathematic model of electrohydraulic proportional system was achieved. A discontinuous projection based on an adaptive robust controller to approximate the nonlinear gain coefficient of the valve was presented to deal with the nonlinearity of the whole system, the error was dealt with by robust feedback and an adaptive robust controller was designed. The experiment results of the boom motion control show that, using the controller, good performance for tracking can be achieved, and the peak tracking error of boom angles is less than 4°.
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.
文摘This work deals with analysis of dynamic behaviour of hydraulic excavator on the basis of developed dynamic-mathematical model.The mathematical model with maximum five degrees of freedom is extended by new generalized coordinate which represents rotation around transversal main central axis of inertia of undercarriage.The excavator is described by a system of six nonlinear,nonhomogenous differential equations of the second kind.Numerical analysis of the differential equations has been done for BTH-600 hydraulic excavator with moving mechanism with pneumatic wheels.
基金Project(2003AA430200)supported by the National High Technology Research and Development Program of China
文摘In order to measure the backhoe vibratory excavating resistance of a hydraulic excavator fast and precisely,the influences of vibratory excavating depth,angle,vibratory frequency,amplitude,bucket inserting velocity and soil type on the vibratory excavating resistance were analyzed.Simulation analysis was carded out to establish the bucket inserting velocity,amplitude and vibratory frequency considered as secondary variables and excavating resistance as primary variable.A fttzzy membership function was introduced to improve the anti-noise capacity of support vector machine,which is a soft-sensing model on the hydraulic excavator＇s backhoe vibratory excavating resistance based on fuzzy support vector machine.The simulation result reveals that its maximum relative training and testing error are nearly 0.68% and-0.47%,respectively.It is concluded that the model has quite high modeling precision and generalization capacity,and it can measure the vibratory excavating resistance accurately,reliably and fast in an indirect way.
