Uncertain loads of the rigid-soft hybrid manipulator directly affect working configurations,which will alter the system model parameters,and thereby degrade control accuracy and efficiency.This paper introduces an eve...Uncertain loads of the rigid-soft hybrid manipulator directly affect working configurations,which will alter the system model parameters,and thereby degrade control accuracy and efficiency.This paper introduces an event-triggered adaptive model predictive control strategy,which integrates with a data-driven approach to control hybrid robots with a cable-driven soft component.In the presence of model uncertainty and mismatch,adaptive identification is employed to improve the nominal model within the controller.Meanwhile,an event-triggered scheme is utilized to reduce redundant identification frequency and improve computing efficiency.Furthermore,an online data-driven method,called input mapping,uses the relationship between the historical input and output data to compensate for the minor model error in the controller via linear combination.The optimization problem is efficiently solved by designing the attenuation coefficient in an infinite-domain situation.Comparative simulation and experimental results demonstrate that the proposed method achieves improved accuracy and faster convergence speed.展开更多
基金supported by the China Postdoctoral Science Foundation (No.2025M771696)。
文摘Uncertain loads of the rigid-soft hybrid manipulator directly affect working configurations,which will alter the system model parameters,and thereby degrade control accuracy and efficiency.This paper introduces an event-triggered adaptive model predictive control strategy,which integrates with a data-driven approach to control hybrid robots with a cable-driven soft component.In the presence of model uncertainty and mismatch,adaptive identification is employed to improve the nominal model within the controller.Meanwhile,an event-triggered scheme is utilized to reduce redundant identification frequency and improve computing efficiency.Furthermore,an online data-driven method,called input mapping,uses the relationship between the historical input and output data to compensate for the minor model error in the controller via linear combination.The optimization problem is efficiently solved by designing the attenuation coefficient in an infinite-domain situation.Comparative simulation and experimental results demonstrate that the proposed method achieves improved accuracy and faster convergence speed.
