Unified Mapping Function-Based Neuroadaptive Control of Constrained Uncertain Robotic Systems

• DOI: 10.1109/tcyb.2021.3135893
• Publication Date: 2022-01-04T20:30:31Z
• Authors: Kai Zhao; Long Chen; Wenchao Meng; Lin Zhao
• Keywords: Position (finance)

For the existing adaptive constrained robotic control algorithms, demanding "feasibility conditions" on virtual controller is normally inevitable and extra limits constraining functions have to be imposed, making corresponding approaches more less user friendly in development. Here, we develop a new neuroadaptive strategy for uncertain manipulators presence of position velocity constraints. First, novel unified mapping function (UMF) constructed so that restriction boundaries removed kinds forms can handled. Second, by integrating UMF-based coordinate transformation with "universal" approximation characteristic neural networks over some compact set, developed completely obviates complicated yet undesired conditions." Furthermore, it proven all closed-loop signals are semiglobally bounded constraints not violated. The effectiveness proposed validated via two-link rigid manipulator.