# Iterative Learning Control Without Resetting Conditions of an Algorithm Based on a Finite-Time Zeroing Neural Network

## TL;DR

This paper introduces a new control method for robots that improves tracking accuracy and reduces the need for reset conditions during repetitive tasks.

## Contribution

The novel NRCILC-FTZNN method eliminates reset conditions and improves convergence in trajectory tracking under disturbances.

## Key findings

- The proposed method reduces tracking errors by 46.89% and 63.29% compared to other schemes.
- Tracking errors converge to zero in fewer iterations than existing methods.
- Simulation results confirm the effectiveness of the NRCILC-FTZNN under disturbances.

## Abstract

In this paper, an iterative learning control without resetting conditions based on a finite-time zeroing neural network (NRCILC-FTZNN) is designed for trajectory tracking of a robotic manipulator operating under external disturbances and executing repetitive tasks. A finite-time zeroing neural network (FTZNN) is developed to eliminate external disturbances and enhance convergence. Furthermore, an iterative learning control without resetting conditions based on the FTZNN is proposed to automatically provide the initial state value in each iteration, thereby eliminating the need for reset conditions. The trajectory-tracking errors, measured by the mean absolute error (MAE), are reduced by 46.89% and 63.29% compared to other schemes. Furthermore, the tracking errors of the proposed NRCILC-FTZNN method converge to zero in fewer iterations than those of the other methods. Simulation results demonstrate the convergence of the robotic manipulator system under disturbances to confirm the effectiveness of NRCILC-FTZNN scheme.