Robot interaction control with variable impedance parameters may conform to task requirements during continuous interaction with dynamic environments.Iterative learning(IL)is effective to learn desired impedance param...Robot interaction control with variable impedance parameters may conform to task requirements during continuous interaction with dynamic environments.Iterative learning(IL)is effective to learn desired impedance parameters for robots under unknown environments,and Gaussian process(GP)is a nonparametric Bayesian approach that models complicated functions with provable confidence using limited data.In this paper,we propose an impedance IL method enhanced by a sparse online Gaussian process(SOGP)to speed up learning convergence and improve generalization.The SOGP for variable impedance modeling is updated in the same iteration by removing similar data points from previous iterations while learning impedance parameters in multiple iterations.The proposed IL-SOGP method is verified by high-fidelity simulations of a collaborative robot with 7 degrees of freedom based on the admittance control framework.It is shown that the proposed method accelerates iterative convergence and improves generalization compared to the classical IL-based impedance learning method.展开更多
The Virtual Resistor based Active Damping(VR-AD) is widely employed in converters connected to the grid via LCL filters in order to mitigate the inherent resonance of the filters. Nevertheless, in digitally controlled...The Virtual Resistor based Active Damping(VR-AD) is widely employed in converters connected to the grid via LCL filters in order to mitigate the inherent resonance of the filters. Nevertheless, in digitally controlled systems, the PWM and the calculating delays modify the system characteristics in terms of frequency and phase, thus destabilizing the system and degrading the VR-AD performances, mainly in low switching frequencies. Moreover, the stability of the system is greatly affected under weak grid operation characterized by large grid impedance variation. This paper solves these problems by proposing a systematic, robust and optimized design procedure of voltage oriented PI control(VOC) with VRAD. The considered design procedure ensures robust control(sufficient stability margins) and high quality of grid current(reduced steady-state error and minimized THD value) despite the negative impact of digital time delay, grid impedance variation and filter parameters change. Simulation and experimental results are presented to show robustness and efficiency of the suggested design procedure.展开更多
基金supported in part by the National Research Foundation of Korea(NRF)Grant Funded by the Korea Government(MSIT)(RS-2025-00555064).Recommended by Associate Editor Zengguang Hou.
文摘Robot interaction control with variable impedance parameters may conform to task requirements during continuous interaction with dynamic environments.Iterative learning(IL)is effective to learn desired impedance parameters for robots under unknown environments,and Gaussian process(GP)is a nonparametric Bayesian approach that models complicated functions with provable confidence using limited data.In this paper,we propose an impedance IL method enhanced by a sparse online Gaussian process(SOGP)to speed up learning convergence and improve generalization.The SOGP for variable impedance modeling is updated in the same iteration by removing similar data points from previous iterations while learning impedance parameters in multiple iterations.The proposed IL-SOGP method is verified by high-fidelity simulations of a collaborative robot with 7 degrees of freedom based on the admittance control framework.It is shown that the proposed method accelerates iterative convergence and improves generalization compared to the classical IL-based impedance learning method.
基金supported by the Tunisian Ministry of High Education and Research under Grant LSE-ENIT-LR11ES15
文摘The Virtual Resistor based Active Damping(VR-AD) is widely employed in converters connected to the grid via LCL filters in order to mitigate the inherent resonance of the filters. Nevertheless, in digitally controlled systems, the PWM and the calculating delays modify the system characteristics in terms of frequency and phase, thus destabilizing the system and degrading the VR-AD performances, mainly in low switching frequencies. Moreover, the stability of the system is greatly affected under weak grid operation characterized by large grid impedance variation. This paper solves these problems by proposing a systematic, robust and optimized design procedure of voltage oriented PI control(VOC) with VRAD. The considered design procedure ensures robust control(sufficient stability margins) and high quality of grid current(reduced steady-state error and minimized THD value) despite the negative impact of digital time delay, grid impedance variation and filter parameters change. Simulation and experimental results are presented to show robustness and efficiency of the suggested design procedure.
