A dynamic velocity feed-forward compensation control (DVFCC) approach with RBF neural network (RBF-NN) dynamic model identification was presented for the adaptive trajectory tracking of industrial robots.The proposed ...A dynamic velocity feed-forward compensation control (DVFCC) approach with RBF neural network (RBF-NN) dynamic model identification was presented for the adaptive trajectory tracking of industrial robots.The proposed control approach combined the advantages of traditional feedback closed-loop position control and computed torque control based on inverse dynamic model.The feed-forward compensator used a nominal robot dynamics as accurate dynamic model and on-line identification with RBF-NN as uncertain part to improve dynamic modeling accuracy.The proposed compensation was applied as velocity feed-forward by an inverse velocity controller that can convert torque signal into velocity in the standard industrial controller.Then,the need for a torque control interface was avoided in the real-time dynamic control of industrial robot.The simulations and experiments were carried out on a gas cutting manipulator.The results show that the proposed control approach can reduce steady-state error,suppress overshoot and enhance tracking accuracy and efficiency in joint space and Cartesian space,especially under highspeed condition.展开更多
Magnetically coupled rodless cylinders are widely used in the coordinate positioning of mechanical arms,electro-static paintings,and other industrial applications.However,they exhibit strong nonlinear characteristics,...Magnetically coupled rodless cylinders are widely used in the coordinate positioning of mechanical arms,electro-static paintings,and other industrial applications.However,they exhibit strong nonlinear characteristics,which lead to low servo control accuracy.In this study,a mass-flow equation through the valve port was derived to improve the control performance,considering the characteristics of the dynamics and throttle-hole flow.Subsequently,a fric-tion model combining static,viscous,and Coulomb friction with a zero-velocity interval was proposed.In addition,energy and dynamic models were set for the experimental investigation of the magnetically coupled rodless cylin-der.A nonlinear mathematical model for the position of the magnetically coupled rodless cylinder was proposed.An incremental PID controller was designed for the magnetically coupled rodless cylinder to control this system,and the PID parameters were adjusted online using RBF neural network.The response results of the PID parameters based on the RBF neural network were compared with those of the traditional incremental PID control,which proved the superiority of the optimization control algorithm of the incremental PID parameters based on the RBF neural network servo control system.The experimental results of this model were compared with the simulation results.The average error between the established model and the actual system was 0.005175054(m),which was approximately 2.588%of the total travel length,demonstrating the accuracy of the theoretical model.展开更多
Hysteresis widely exists in civil structures,and dissipates the mechanical energy of systems.Research on the random vibration of hysteretic systems,however,is still insufficient,particularly when the excitation is non...Hysteresis widely exists in civil structures,and dissipates the mechanical energy of systems.Research on the random vibration of hysteretic systems,however,is still insufficient,particularly when the excitation is non-Gaussian.In this paper,the radial basis function(RBF)neural network(RBF-NN)method is adopted as a numerical method to investigate the random vibration of the Bouc-Wen hysteretic system under the Poisson white noise excitations.The solution to the reduced generalized Fokker-PlanckKolmogorov(GFPK)equation is expressed in terms of the RBF-NNs with the Gaussian activation functions,whose weights are determined by minimizing the loss function of the reduced GFPK equation residual and constraint associated with the normalization condition.A steel fiber reinforced ceramsite concrete(SFRCC)column loaded by the Poisson white noise is studied as an example to illustrate the solution process.The effects of several important parameters of both the system and the excitation on the stochastic response are evaluated,and the obtained results are compared with those obtained by the Monte Carlo simulations(MCSs).The numerical results show that the RBF-NN method can accurately predict the stationary response with a considerable high computational efficiency.展开更多
准确的能源负荷预测对综合能源系统的经济调度和优化运行有着重要的影响。提出一种基于径向基函数神经网络(radial basis function neural network,RBF-NN)模型的综合能源系统电、气、热多元负荷短期预测方法。首先利用Copula理论对电...准确的能源负荷预测对综合能源系统的经济调度和优化运行有着重要的影响。提出一种基于径向基函数神经网络(radial basis function neural network,RBF-NN)模型的综合能源系统电、气、热多元负荷短期预测方法。首先利用Copula理论对电、气、热负荷进行相关性分析,建立了电、气、热负荷和温度的时间序列;接着设计RBF.NN网络模型结构,采用K.means聚类算法对隐含层节点进行优化;最后通过国内某园区综合能源系统的实际数据对模型进行验证。通过3个案例结果的比较,验证了文中提出的方法可以有效地考虑电、气、热负荷之间的耦合关系,具有较高的预测精度。展开更多
文摘A dynamic velocity feed-forward compensation control (DVFCC) approach with RBF neural network (RBF-NN) dynamic model identification was presented for the adaptive trajectory tracking of industrial robots.The proposed control approach combined the advantages of traditional feedback closed-loop position control and computed torque control based on inverse dynamic model.The feed-forward compensator used a nominal robot dynamics as accurate dynamic model and on-line identification with RBF-NN as uncertain part to improve dynamic modeling accuracy.The proposed compensation was applied as velocity feed-forward by an inverse velocity controller that can convert torque signal into velocity in the standard industrial controller.Then,the need for a torque control interface was avoided in the real-time dynamic control of industrial robot.The simulations and experiments were carried out on a gas cutting manipulator.The results show that the proposed control approach can reduce steady-state error,suppress overshoot and enhance tracking accuracy and efficiency in joint space and Cartesian space,especially under highspeed condition.
基金Supported by Outstanding Young Scientists in Beijing of China(Grant No.BJJWZYJH01201910006021)Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems of China(Grant No.GZKF-202016)+2 种基金Henan Provincial Science and Technology Key Project of China(Grant Nos.202102210081,212102210050)Sub Project of Strengthening Key Basic Research Projects in the Basic Plan of the Science and Technology Commission of the Central Military Commission of China(Grant No.2019-JCJQ-ZD-120-13)Henan Provincial Fundamental Research Funds for the Universities of China(Grant No.NSFRF200403).
文摘Magnetically coupled rodless cylinders are widely used in the coordinate positioning of mechanical arms,electro-static paintings,and other industrial applications.However,they exhibit strong nonlinear characteristics,which lead to low servo control accuracy.In this study,a mass-flow equation through the valve port was derived to improve the control performance,considering the characteristics of the dynamics and throttle-hole flow.Subsequently,a fric-tion model combining static,viscous,and Coulomb friction with a zero-velocity interval was proposed.In addition,energy and dynamic models were set for the experimental investigation of the magnetically coupled rodless cylin-der.A nonlinear mathematical model for the position of the magnetically coupled rodless cylinder was proposed.An incremental PID controller was designed for the magnetically coupled rodless cylinder to control this system,and the PID parameters were adjusted online using RBF neural network.The response results of the PID parameters based on the RBF neural network were compared with those of the traditional incremental PID control,which proved the superiority of the optimization control algorithm of the incremental PID parameters based on the RBF neural network servo control system.The experimental results of this model were compared with the simulation results.The average error between the established model and the actual system was 0.005175054(m),which was approximately 2.588%of the total travel length,demonstrating the accuracy of the theoretical model.
基金the National Natural Science Foundation of China(No.12072118)the Natural Science Funds for Distinguished Young Scholar of Fujian Province of China(No.2021J06024)the Project for Youth Innovation Fund of Xiamen of China(No.3502Z20206005)。
文摘Hysteresis widely exists in civil structures,and dissipates the mechanical energy of systems.Research on the random vibration of hysteretic systems,however,is still insufficient,particularly when the excitation is non-Gaussian.In this paper,the radial basis function(RBF)neural network(RBF-NN)method is adopted as a numerical method to investigate the random vibration of the Bouc-Wen hysteretic system under the Poisson white noise excitations.The solution to the reduced generalized Fokker-PlanckKolmogorov(GFPK)equation is expressed in terms of the RBF-NNs with the Gaussian activation functions,whose weights are determined by minimizing the loss function of the reduced GFPK equation residual and constraint associated with the normalization condition.A steel fiber reinforced ceramsite concrete(SFRCC)column loaded by the Poisson white noise is studied as an example to illustrate the solution process.The effects of several important parameters of both the system and the excitation on the stochastic response are evaluated,and the obtained results are compared with those obtained by the Monte Carlo simulations(MCSs).The numerical results show that the RBF-NN method can accurately predict the stationary response with a considerable high computational efficiency.
文摘准确的能源负荷预测对综合能源系统的经济调度和优化运行有着重要的影响。提出一种基于径向基函数神经网络(radial basis function neural network,RBF-NN)模型的综合能源系统电、气、热多元负荷短期预测方法。首先利用Copula理论对电、气、热负荷进行相关性分析,建立了电、气、热负荷和温度的时间序列;接着设计RBF.NN网络模型结构,采用K.means聚类算法对隐含层节点进行优化;最后通过国内某园区综合能源系统的实际数据对模型进行验证。通过3个案例结果的比较,验证了文中提出的方法可以有效地考虑电、气、热负荷之间的耦合关系,具有较高的预测精度。
