Dec 10, 2019 · This paper proposes a novel robust adaptive-backstepping-recurrent-fuzzy-wavelet-neural-networks controller (ABRFWNNs) based on dead zone ...
This paper proposes a novel robust adaptive-backstepping-recurrent-fuzzy-wavelet-neural-networks controller (ABRFWNNs) based on dead zone compensator for ...
A novel robust adaptive-backstepping-recurrent-fuzzy-wavelet-neural-networks controller based on dead zone compensator for Industrial Robot Manipulators ...
Dec 10, 2019 · This paper proposes a novel robust adaptive-backstepping-recurrent-fuzzy-wavelet-neural-networks controller (ABRFWNNs) based on dead zone ...
Dec 10, 2019 · In this paper, a robust-adaptive-fuzzy-neural-network controller (RAFNNs) bases on dead zone compensator for industrial robot manipulators (RM) ...
Abstract. This paper proposes a novel robust adaptive-backstepping-recurrent-fuzzy-wavelet-neural-networks controller (ABRFWNNs) based on dead zone compensator ...
A novel Robust Adaptive Control Using RFWNNs and Backstepping for Industrial Robot Manipulators with Dead-Zone ... Control of Dynamic Plants—A Novel Structure and ...
This paper proposes a novel robust adaptive-backstepping-recurrent-fuzzy-wavelet-neural-networks controller (ABRFWNNs) based on dead zone compensator.
Apr 23, 2021 · In this paper, a robust-adaptive-fuzzy-neural-network controller (RAFNNs) bases on dead zone compensator for industrial robot manipulators ...
People also ask
What is adaptive control of robotic manipulator?
An adaptive control scheme is developed for a robot manipulator to track a desired trajectory as closely as possible in spite of a wide range of manipulator motions and parameter uncertainties of links and payload. The presented control scheme has two components: a nominal control and a variational control.
Why is an adaptive controller preferred over a conventional controller?
The primary benefit of adaptive control is its flexibility and ability to perform well in unpredictable environments. It can compensate for disturbances, uncertainties, and changes in system dynamics, making it ideal for complex, non-linear, or time-varying systems.
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