In this paper, we reconstruct the superprocesses of stochastic flows by martingale method, and prove that if and only if the infinitesimal particles never hit each other, then atomic part and diffuse part of this kind...In this paper, we reconstruct the superprocesses of stochastic flows by martingale method, and prove that if and only if the infinitesimal particles never hit each other, then atomic part and diffuse part of this kind of superprocesses will be also superprocesses of stochastic flows. This result completely answers the open problem in .展开更多
Designing a controller for the docking maneuver in Probe-Drogue Refueling(PDR) is an important but challenging task, due to the complex system model and the high precision requirement.In order to overcome the disadvan...Designing a controller for the docking maneuver in Probe-Drogue Refueling(PDR) is an important but challenging task, due to the complex system model and the high precision requirement.In order to overcome the disadvantage of only feedback control, a feedforward control scheme known as Iterative Learning Control(ILC) is adopted in this paper.First, Additive State Decomposition(ASD) is used to address the tight coupling of input saturation, nonlinearity and the property of Non Minimum Phase(NMP) by separating these features into two subsystems(a primary system and a secondary system).After system decomposition, an adjoint-type ILC is applied to the Linear Time-Invariant(LTI) primary system with NMP to achieve entire output trajectory tracking, whereas state feedback is used to stabilize the secondary system with input saturation.The two controllers designed for the two subsystems can be combined to achieve the original control goal of the PDR system.Furthermore, to compensate for the receiverindependent uncertainties, a correction action is proposed by using the terminal docking error,which can lead to a smaller docking error at the docking moment.Simulation tests have been carried out to demonstrate the performance of the proposed control method, which has some advantages over the traditional derivative-type ILC and adjoint-type ILC in the docking control of PDR.展开更多
The repetitive control(RC) or repetitive controller problem for nonminimum phase nonlinear systems is both challenging and practical. In this paper, we consider an RC problem for the translational oscillator with a ro...The repetitive control(RC) or repetitive controller problem for nonminimum phase nonlinear systems is both challenging and practical. In this paper, we consider an RC problem for the translational oscillator with a rotational actuator(TORA), which is a nonminimum phase nonlinear system. The major difficulty is to handle both a nonminimum phase RC problem and a nonlinear problem simultaneously. For such purpose, a new RC design, namely the additive-state-decomposition-based approach, is proposed,by which the nonminimum phase RC problem and the nonlinear problem are separated. This makes RC for the TORA benchmark tractable. To demonstrate the effectiveness of the proposed approach, a numerical simulation is given.展开更多
基金Supported by the Nature Science Foundation of Henan(2004601018)
文摘In this paper, we reconstruct the superprocesses of stochastic flows by martingale method, and prove that if and only if the infinitesimal particles never hit each other, then atomic part and diffuse part of this kind of superprocesses will be also superprocesses of stochastic flows. This result completely answers the open problem in .
基金supported by the National Natural Science Foundation of China(No.61473012)。
文摘Designing a controller for the docking maneuver in Probe-Drogue Refueling(PDR) is an important but challenging task, due to the complex system model and the high precision requirement.In order to overcome the disadvantage of only feedback control, a feedforward control scheme known as Iterative Learning Control(ILC) is adopted in this paper.First, Additive State Decomposition(ASD) is used to address the tight coupling of input saturation, nonlinearity and the property of Non Minimum Phase(NMP) by separating these features into two subsystems(a primary system and a secondary system).After system decomposition, an adjoint-type ILC is applied to the Linear Time-Invariant(LTI) primary system with NMP to achieve entire output trajectory tracking, whereas state feedback is used to stabilize the secondary system with input saturation.The two controllers designed for the two subsystems can be combined to achieve the original control goal of the PDR system.Furthermore, to compensate for the receiverindependent uncertainties, a correction action is proposed by using the terminal docking error,which can lead to a smaller docking error at the docking moment.Simulation tests have been carried out to demonstrate the performance of the proposed control method, which has some advantages over the traditional derivative-type ILC and adjoint-type ILC in the docking control of PDR.
基金supported by National Natural Science Foundation of China(No.61473012)
文摘The repetitive control(RC) or repetitive controller problem for nonminimum phase nonlinear systems is both challenging and practical. In this paper, we consider an RC problem for the translational oscillator with a rotational actuator(TORA), which is a nonminimum phase nonlinear system. The major difficulty is to handle both a nonminimum phase RC problem and a nonlinear problem simultaneously. For such purpose, a new RC design, namely the additive-state-decomposition-based approach, is proposed,by which the nonminimum phase RC problem and the nonlinear problem are separated. This makes RC for the TORA benchmark tractable. To demonstrate the effectiveness of the proposed approach, a numerical simulation is given.
