In this paper,a new model identification method is developed for a class of delay fractional-order system based on the process step response.Four characteristic functions are defined to characterize the features of th...In this paper,a new model identification method is developed for a class of delay fractional-order system based on the process step response.Four characteristic functions are defined to characterize the features of the normalized fractionalorder model.Based on the time scaling technology,two identification schemes are proposed for parameters' estimation.The scheme one utilizes three exact points on the step response of the process to calculate model parameters directly.The other scheme employs optimal searching method to adjust the fractional order for the best model identification.The proposed two identification schemes are both applicable to any stable complex process,such as higher-order,under-damped/over-damped,and minimum-phase/nonminimum-phase processes.Furthermore,an optimal PID tuning method is proposed for the delay fractionalorder systems.The requirements on the stability margins and the negative feedback are cast as real part constraints(RPC)and imaginary part constraints(IPC).The constraints are implemented by trigonometric inequalities on the phase variable,and the optimal PID controller is obtained by the minimization of the integral of time absolute error(ITAE) index.Identification and control of a Titanium billet heating process is given for the illustration.展开更多
A hybrid position/force controller is designed for the joint 2 and the joint 3 of thePUMA 560 robot.The hybrid controller includes a multilayered neural network,which canidentify the dynamics of the contacted environm...A hybrid position/force controller is designed for the joint 2 and the joint 3 of thePUMA 560 robot.The hybrid controller includes a multilayered neural network,which canidentify the dynamics of the contacted environment and can optimize the parameters of PIDcontroller.The experimental results show that after having been trained,the robot has sta-ble response to the training patterns and strong adaptive ability to the situation between thepatterns.展开更多
基金supported by National Natural Science Foundation of China(61403149,61573298)Natural Science Foundation of Fujian Province(2015J01261,2016J05165)Foundation of Huaqiao University(Z14Y0002)
文摘In this paper,a new model identification method is developed for a class of delay fractional-order system based on the process step response.Four characteristic functions are defined to characterize the features of the normalized fractionalorder model.Based on the time scaling technology,two identification schemes are proposed for parameters' estimation.The scheme one utilizes three exact points on the step response of the process to calculate model parameters directly.The other scheme employs optimal searching method to adjust the fractional order for the best model identification.The proposed two identification schemes are both applicable to any stable complex process,such as higher-order,under-damped/over-damped,and minimum-phase/nonminimum-phase processes.Furthermore,an optimal PID tuning method is proposed for the delay fractionalorder systems.The requirements on the stability margins and the negative feedback are cast as real part constraints(RPC)and imaginary part constraints(IPC).The constraints are implemented by trigonometric inequalities on the phase variable,and the optimal PID controller is obtained by the minimization of the integral of time absolute error(ITAE) index.Identification and control of a Titanium billet heating process is given for the illustration.
基金supported by the National Nature Science Foundation of China(61304223)the Aeronautical Science Foundation of China(2016ZA52009)the Research Fund for the Doctoral Program of Higher Education of China(20123218120015)
基金Supported by the National Defence Science & Technology Pre-research Fund of China.
文摘A hybrid position/force controller is designed for the joint 2 and the joint 3 of thePUMA 560 robot.The hybrid controller includes a multilayered neural network,which canidentify the dynamics of the contacted environment and can optimize the parameters of PIDcontroller.The experimental results show that after having been trained,the robot has sta-ble response to the training patterns and strong adaptive ability to the situation between thepatterns.