Paper
23 May 2023 Trajectory tracking control of manipulator with variable length error via iterative learning and neural network scheme
Rongjia Tang, Yongchun Liu
Author Affiliations +
Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126452L (2023) https://doi.org/10.1117/12.2681662
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China
Abstract
In this paper, trajectory tracking of uncertain manipulator under arbitrary initial conditions is investigated. Firstly, the error dynamics is established according to the preset expected trajectory, the probability distribution function of random variables and the virtual error variable. Secondly, the virtual control signal and iterative learning are used to compensate the interval without operation and the iteration length with random changes. Then, the uncertainty and external disturbance of the manipulator arms are approximated by an adaptive neural network, and the practicability of the tracking algorithm is inspected by a composite energy function. Finally, the simulation example is certified to display the validity of the proposed algorithm.
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Rongjia Tang and Yongchun Liu "Trajectory tracking control of manipulator with variable length error via iterative learning and neural network scheme", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126452L (23 May 2023); https://doi.org/10.1117/12.2681662
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KEYWORDS
Neural networks

Control systems

Matrices

Adaptive control

Complex systems

Detection and tracking algorithms

Process control

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