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

For the existing adaptive constrained robotic control algorithms, the demanding "feasibility conditions" on virtual controller is normally inevitable and the extra limits on constraining functions have to be imposed, making the corresponding approaches more demanding and less user friendly...

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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 53; no. 6; pp. 3665 - 3674
Main Authors: Zhao, Kai, Chen, Long, Meng, Wenchao, Zhao, Lin
Format: Journal Article
Language:English
Published: United States IEEE 01.06.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive control
Algorithms
Artificial neural networks
Closed loops
Constraining
Constraints
Control algorithms
Control design
Coordinate transformations
Feasibility
Heuristic algorithms
Manipulator dynamics
Manipulators
Mapping
Motion constraints
Neural networks
neuroadaptive control
Robot arms
Robot control
Robot kinematics
robotic systems
Robotics
Robots
Service robots
unified mapping function
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
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Summary:For the existing adaptive constrained robotic control algorithms, the demanding "feasibility conditions" on virtual controller is normally inevitable and the extra limits on constraining functions have to be imposed, making the corresponding approaches more demanding and less user friendly in control development. Here, we develop a new neuroadaptive constrained control strategy for uncertain robotic manipulators in the presence of position and velocity constraints. First, a novel unified mapping function (UMF) is constructed so that the restriction on constraining boundaries is removed and more kinds of constraining forms can be handled. Second, by integrating the UMF-based coordinate transformation with the "universal" approximation characteristic of neural networks over some compact set, the developed neuroadaptive control completely obviates the complicated yet undesired "feasibility conditions." Furthermore, it is proven that all closed-loop signals are semiglobally bounded and the constraints are not violated. The effectiveness of the proposed control is validated via a two-link rigid robotic manipulator.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2021.3135893