首页|Findings from Zhengzhou University Provides New Data on Robotics (Task-oriented Adaptive Position/force Control for Robotic Systems Under Hybrid Constraints)
Findings from Zhengzhou University Provides New Data on Robotics (Task-oriented Adaptive Position/force Control for Robotic Systems Under Hybrid Constraints)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Robotics are disc ussed in a new report. According to news reporting originating in Zhengzhou, Peo ple's Republic of China, by NewsRx journalists, research stated, "By mapping the performances of the task requirement and the inherent physical characteristics of the robotic systems to the hybrid constraints, this article proposes a task-o riented adaptive position/force control (TOAPFC) scheme for the robotic systems to ensure the execution of the predefined tasks and the safety of robotic manipu lators and humans in the task workspace. In the proposed scheme, a reference tra jectory generation strategy and admittance model are regarded as the outer loop of TOAPFC to obtain and shape the robotic system's task trajectory that guarante es the safety of the interaction system." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news reporters obtained a quote from the research from Zhengzhou University, "An admittancebased adaptive position/force control scheme unifying the positi on and force into a control law is used as the inner loop of TOAPFC to track the shaped task trajectory, where a barrier Lyapunov function is utilized to constr ain the tracking errors within permitted ranges. Moreover, the system uncertaint ies and lumped disturbances are compensated by the radial basis function neural network and robust compensator, respectively. Meanwhile, the stability of the pr oposed admittance-based adaptive position/force control scheme is analyzed by us ing the Lyapunov stability theory."
ZhengzhouPeople's Republic of ChinaA siaEmerging TechnologiesMachine LearningRoboticsRobotsZhengzhou Univer sity
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