Development of an Adaptive Sliding Mode Controller using Neural Networks for Trajectory Tracking of a Cylindrical Manipulator

TL;DR

This paper presents an adaptive sliding mode controller integrated with neural networks to enhance trajectory tracking accuracy and robustness in cylindrical manipulators, addressing uncertainties in nonlinear control systems for industrial automation.

Contribution

It introduces a novel ASMC-NN approach that combines sliding mode control with neural networks to improve control performance under uncertainties.

Findings

Achieves high trajectory tracking accuracy

Demonstrates fast response time

Enhances reliability in control performance

Abstract

Cylindrical manipulators are extensively used in industrial automation, especially in emerging technologies like 3D printing, which represents a significant future trend. However, controlling the trajectory of nonlinear models with system uncertainties remains a critical challenge, often leading to reduced accuracy and reliability. To address this, the study develops an Adaptive Sliding Mode Controller (ASMC) integrated with Neural Networks (NNs) to improve trajectory tracking for cylindrical manipulators. The ASMC leverages the robustness of sliding mode control and the adaptability of neural networks to handle uncertainties and dynamic variations effectively. Simulation results validate that the proposed ASMC-NN achieves high trajectory tracking accuracy, fast response time, and enhanced reliability, making it a promising solution for applications in 3D printing and beyond.