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Extensive numerical results are obtained to show that the neural network model together with the sliding mode control exhibits outstanding performance, achieving a trajectory tracking error below 5 cm on average for the Delta robot.
Mar 8, 2024 · The neural networks are trained with randomly sampled data in a sufficiently large workspace. The sliding mode control for trajectory tracking ...
Evaluation of Neural Network Effectiveness on Sliding Mode Control of Delta Robot for Trajectory Tracking. ... "Evaluation of Neural Network Effectiveness ...
... Evaluation of Neural Network Effectiveness on Sliding Mode Control of Delta Robot for Trajectory Tracking | The Delta robot is an over-actuated parallel robot ...
Apr 30, 2024 · Abstract - In this paper, we propose the application of the backstepping sliding mode control method for the Delta robot, aiming.
Dec 28, 2023 · These studies demonstrate the effectiveness of using NNs in combination with SMC/TSMC methods for controlling robot manipulators and show how ...
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Apr 23, 2024 · This paper proposes a data-driven optimal tracking control scheme for unknown general nonlinear systems using neural networks.
In this paper, we propose the application of the backstepping sliding mode control method for the Delta robot, aiming to achieve desired motion trajectories ...
This paper proposes an online Neural Network self-tuned Inverse Dynamic Controller (IDC) for high-speed and smooth trajectory tracking control of a 3-DoF ...
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Dec 30, 2022 · The influence of the neural network on the control performance is evaluated by comparing the proposed intelligent approach with a conventional ...