# Improved Adaptive Sliding Mode Control Using Quasi-Convex Functions and Neural Network-Assisted Time-Delay Estimation for Robotic Manipulators

## TL;DR

This paper introduces a new adaptive control method for robotic manipulators using quasi-convex functions and neural networks to reduce chattering and improve stability.

## Contribution

A novel adaptive sliding mode control method with quasi-convex functions and neural network-assisted time-delay estimation is proposed.

## Key findings

- The proposed method effectively suppresses chattering in robotic manipulators.
- Simulation and experiment results confirm the stability and effectiveness of the control strategy.

## Abstract

This study presents an adaptive sliding mode control strategy tailored for robotic manipulators, featuring a quasi-convex function-based control gain and a time-delay estimation (TDE) enhanced by neural networks. To compensate for TDE errors, the proposed method utilizes both the previous TDE error and radial basis function neural networks with a weight update law that includes damping terms to prevent divergence. Additionally, a continuous gain function that is quasi-convex function dependent on the magnitude of the sliding variable is proposed to replace the traditional switching control gain. This continuous function-based gain has effectiveness in suppressing chattering phenomenon while guaranteeing the stability of the robotic manipulator in terms of uniform ultimate boundedness, which is demonstrated through both simulation and experiment results.