Wavelet neural network sliding mode control of two rigid joint robot manipulator

Abstract

To solve the problems of low accuracy and poor stability due to uncertainties, external disturbances and unknown load, which exist in the position control of rigid joint robot manipulator, this article is to propose Non-Singular Fast Terminal Sliding Mode Control strategy with Wavelet neural networks observer (NSFTSMCW). The wavelet observer is designed using the online approximation capability of the neural network, which is used to online estimate the modeling error, external disturbances and uncertainties generated by the dynamic surface control of the joint robot online. Combining the above strategies, the robot manipulators position controller is designed. The stability of this control strategy is demonstrated by stability analysis using the Lyapunov criterion. Simulations on the 2-Link Rigid Joint (2LRJ) robot show that the control strategy can overcome the chattering phenomena ensures the accuracy and stability of the joint robot position control.

In-Depth Review of Advanced Control Strategies and Cutting-Edge Trends in Robot Manipulators: Analyzing the Latest Developments and Techniques

“…The only drawback or deficiency in this research was the unavailability of hardware validation. Tlijani et al [40] developed an event-triggered strategy-based time delay sliding mode control (ET-TDSMC) scheme and verified its effectiveness using a two-link robot manipulator via numerical simulation and experimental study. The suggested control mechanism yields acceptable results and could be useful in poor network environments.…”

“…It is another ground-breaking technique that brings high-level accuracy for the control methods in the domain of industrial manipulators. Later a non-singular Fast terminal SMC control algorithm was hybridized with a wavelet neural network observer commonly known as NSFTSMCW [40]. In this technique, it takes full responsibility for proper joint position whereas it presents it's a lot of impact in reducing the initial stresses so that the convergence rates may tend to zero as soon as possible.…”

Research on Neural Network Terminal Sliding Mode Control of Robotic Arms Based on Novel Reaching Law and Improved Salp Swarm Algorithm

“…This control method ensures the tracking performance of the system under state constraints and uncertainties, but the design and parameter tuning of the fuzzy neural network are relatively complex. Tlijani et al [23] presented a non-singular fast terminal sliding mode control strategy based on a wavelet neural network observer. A wavelet observer is designed using the online approximation capability of neural networks to estimate modeling errors, external disturbances, and uncertainties in joint robots.…”