Fixed-Time Adaptive Neural Network-Based Trajectory Tracking Control for Workspace Manipulators

Author:

Chen Xiaofei12,Zhao Han1,Zhen Shengchao1,Liu Xiaoxiao2ORCID,Zhang Jinsi2

Affiliation:

1. School of Mechanical Engineering, Hefei University of Technology, Hefei 230000, China

2. School of Electrical and Optoelectronic Engineering, West Anhui University, Liu’an 237000, China

Abstract

This paper proposes a novel neural network-based control algorithm with fixed-time performance constraints for manipulator systems in workspaces. The algorithm efficiently controls the manipulator’s trajectory tracking by tuning a preset performance function, thereby optimizing both speed and accuracy within a fixed timeframe. Initially, a tangent-type error transformation, applied through homogeneous embryonic transformation, ensures rapid convergence of tracking errors to a specific region. Subsequently, integrating a predetermined control strategy into the fixed-time stability framework ensures the system’s state reaches a defined boundary within a finite period. Lastly, neural networks are employed to approximate dynamic parameters and adjust the controller, achieving optimal parameter approximation and significantly enhancing trajectory tracking robustness. Simulation analyses and comparisons confirm the controller’s effectiveness and superiority in enhancing both the transient and steady-state performance of the control system.

Funder

the Scientific Research Startup Fund of West Anhui University

Publisher

MDPI AG

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