This work proposes a novel proportional-derivative(PD)-type state-dependent Riccati equation(SDRE)approach with iterative learning control(ILC)augmentation.On the one hand,the PD-type control gains could adopt many us...This work proposes a novel proportional-derivative(PD)-type state-dependent Riccati equation(SDRE)approach with iterative learning control(ILC)augmentation.On the one hand,the PD-type control gains could adopt many useful available criteria and tools of conventional PD controllers.On the other hand,the SDRE adds nonlinear and optimality characteristics to the controller,i.e.,increasing the stability margins.These advantages with the ILC correction part deliver a precise control law with the capability of error reduction by learning.The SDRE provides a symmetric-positive-definite distributed nonlinear suboptimal gain K(x)for the control input law u=–R–1(x)BT(x)K(x)x.The sub-blocks of the overall gain R–1(x)BT(x)K(x),are not necessarily symmetric positive definite.A new design is proposed to transform the optimal gain into two symmetric-positive-definite gains like PD-type controllers as u=–KSP(x)e–KSD(x)?.The new form allows us to analytically prove the stability of the proposed learning-based controller for mechanical systems;and presents guaranteed uniform boundedness in finite-time between learning loops.The symmetric PD-type controller is also developed for the state-dependent differential Riccati equation(SDDRE)to manipulate the final time.The SDDRE expresses a differential equation with a final boundary condition,which imposes a constraint on time that could be used for finitetime control.So,the availability of PD-type finite-time control is an asset for enhancing the conventional classical linear controllers with this tool.The learning rules benefit from the gradient descent method for both regulation and tracking cases.One of the advantages of this approach is a guaranteed-stability even from the first loop of learning.A mechanical manipulator,as an illustrative example,was simulated for both regulation and tracking problems.Successful experimental validation was done to show the capability of the system in practice by the implementation of the proposed method on a variable-pitch rotor benchmark.展开更多
Robotic splicing of steel arches is a challenging task that is necessary to realize the grasping and docking of steel arches in a limited space.Steel arches often have a mass of more than 200 kg and length of more tha...Robotic splicing of steel arches is a challenging task that is necessary to realize the grasping and docking of steel arches in a limited space.Steel arches often have a mass of more than 200 kg and length of more than 4 m.Owing to the large volume and mass of steel arches and the high requirements for accurately positioning the splicing,it is difficult for a general manipulator to meet the stiffness requirements.To enhance the structural stiffness of the steel arch splicing manipulator,a single-degree-of-freedom(DOF)closed-loop mechanism was added to the grasping structure of the manipulator.Based on the basic principle of structural synthesis,a solution model of the single-DOF closed-loop mechanism was developed,and alternative kinematic pairs of the mechanism with different input constraints and output requirements were derived.Based on this model,a design method for a single-DOF closed-loop grasping mechanism and a posture adjustment mechanism for a steel arch was devised.Combined with the same dimensional subspace equivalence principle of the graphical-type synthesis method,12 types of steel arch splicing manipulator were constructed.By analyzing the motion/force transmission and structural complexity of the steel arch splicing manipulators,the best scheme was selected.A prototype of the steel arch splicing manipulator was manufactured.Adams software was used to obtain clearly the output trajectory of the end of the manipulator.The relative spatial positions of the upper and lower jaws under different working stages were analyzed,demonstrating that the manipulator satisfied the grasping requirements.Through a steel arch splicing experiment,the grasping effect,docking accuracy,and splicing efficiency of the manipulator met the design requirements.The steel arch splicing manipulator can replace the manual completion of the steel arch splicing operation,significantly improving the operation efficiency.展开更多
基金supported by the European Commission H2020 Programme under HYFLIERS project contract 779411AERIAL-CORE project contract number 871479 and the ARTIC(RTI2018-102224-B-I00)projectfunded by the Spanish Agencia Estatal de Investigación。
文摘This work proposes a novel proportional-derivative(PD)-type state-dependent Riccati equation(SDRE)approach with iterative learning control(ILC)augmentation.On the one hand,the PD-type control gains could adopt many useful available criteria and tools of conventional PD controllers.On the other hand,the SDRE adds nonlinear and optimality characteristics to the controller,i.e.,increasing the stability margins.These advantages with the ILC correction part deliver a precise control law with the capability of error reduction by learning.The SDRE provides a symmetric-positive-definite distributed nonlinear suboptimal gain K(x)for the control input law u=–R–1(x)BT(x)K(x)x.The sub-blocks of the overall gain R–1(x)BT(x)K(x),are not necessarily symmetric positive definite.A new design is proposed to transform the optimal gain into two symmetric-positive-definite gains like PD-type controllers as u=–KSP(x)e–KSD(x)?.The new form allows us to analytically prove the stability of the proposed learning-based controller for mechanical systems;and presents guaranteed uniform boundedness in finite-time between learning loops.The symmetric PD-type controller is also developed for the state-dependent differential Riccati equation(SDDRE)to manipulate the final time.The SDDRE expresses a differential equation with a final boundary condition,which imposes a constraint on time that could be used for finitetime control.So,the availability of PD-type finite-time control is an asset for enhancing the conventional classical linear controllers with this tool.The learning rules benefit from the gradient descent method for both regulation and tracking cases.One of the advantages of this approach is a guaranteed-stability even from the first loop of learning.A mechanical manipulator,as an illustrative example,was simulated for both regulation and tracking problems.Successful experimental validation was done to show the capability of the system in practice by the implementation of the proposed method on a variable-pitch rotor benchmark.
基金Supported by Special funding support for the construction of innovative provinces in Hunan Province(Grant No.2019GK1010)National Key R&D Program of China(Grant No.2017YFB1302600).
文摘Robotic splicing of steel arches is a challenging task that is necessary to realize the grasping and docking of steel arches in a limited space.Steel arches often have a mass of more than 200 kg and length of more than 4 m.Owing to the large volume and mass of steel arches and the high requirements for accurately positioning the splicing,it is difficult for a general manipulator to meet the stiffness requirements.To enhance the structural stiffness of the steel arch splicing manipulator,a single-degree-of-freedom(DOF)closed-loop mechanism was added to the grasping structure of the manipulator.Based on the basic principle of structural synthesis,a solution model of the single-DOF closed-loop mechanism was developed,and alternative kinematic pairs of the mechanism with different input constraints and output requirements were derived.Based on this model,a design method for a single-DOF closed-loop grasping mechanism and a posture adjustment mechanism for a steel arch was devised.Combined with the same dimensional subspace equivalence principle of the graphical-type synthesis method,12 types of steel arch splicing manipulator were constructed.By analyzing the motion/force transmission and structural complexity of the steel arch splicing manipulators,the best scheme was selected.A prototype of the steel arch splicing manipulator was manufactured.Adams software was used to obtain clearly the output trajectory of the end of the manipulator.The relative spatial positions of the upper and lower jaws under different working stages were analyzed,demonstrating that the manipulator satisfied the grasping requirements.Through a steel arch splicing experiment,the grasping effect,docking accuracy,and splicing efficiency of the manipulator met the design requirements.The steel arch splicing manipulator can replace the manual completion of the steel arch splicing operation,significantly improving the operation efficiency.
