Intelligent control of a single-link flexible manipulator using sliding modes and artificial neural networks

TL;DR

This paper introduces an adaptive intelligent control method combining sliding mode control and neural networks for precise trajectory tracking of flexible manipulators, capable of real-time learning and handling uncertainties.

Contribution

It presents a novel control scheme that integrates a simplified neural network with sliding mode control, enabling real-time adaptation and improved performance in flexible manipulator control.

Findings

Effective trajectory tracking demonstrated on a flexible manipulator

Robustness against uncertainties and noise confirmed

Real-time learning reduces computational complexity

Abstract

This letter presents a new intelligent control scheme for the accurate trajectory tracking of flexible link manipulators. The proposed approach is mainly based on a sliding mode controller for underactuated systems with an embedded artificial neural network to deal with modeling inaccuracies. The adopted neural network only needs a single input and one hidden layer, which drastically reduces the computational complexity of the control law and allows its implementation in low-power microcontrollers. Online learning, rather than supervised offline training, is chosen to allow the weights of the neural network to be adjusted in real time during the tracking. Therefore, the resulting controller is able to cope with the underactuating issues and to adapt itself by learning from experience, which grants the capacity to deal with plant dynamics properly. The boundedness and convergence properties of the tracking error are proved by evoking Barbalat's lemma in a Lyapunov-like stability analysis. Experimental results obtained with a small single-link flexible manipulator show the efficacy of the proposed control scheme, even in the presence of a high level of uncertainty and noisy signals.