Work

A Novel High‐Voltage‐Wire Stripping Robot and Adaptive Fuzzy RBF Neural Network PID Controller Optimized by PSO‐GA Algorithm

Year: 2026

Type: article

Abstract: ABSTRACT At present, a significant element of the maintenance of high‐voltage transmission lines is the manual stripping of wire. However, manual stripping is a highly risky process. This paper introduces a high‐voltage‐wire stripping robot with the objective of enhancing operator safety, optimizing peeling efficiency, and reducing labor intensity. It is imperative that advanced control strategies be devised in light of the robot's nonlinear and high coupling properties. This paper presents three proportional–integral–derivative (PID) control algorithms for evaluation of the robot: the traditional PID, the fuzzy neural network PID (FNN PID), and the FNN PID control algorithm based on genetic algorithm and particle swarm optimization (PSO‐GA FNN PID). Experiments are conducted with the objective of further demonstrating the superiority of the robot and the various control algorithms in the context of high‐voltage‐wire stripping. Furthermore, the traditional PID, FNN PID, and PSO‐GA FNN PID algorithms are compared. The results demonstrate that the PSO‐GA FNN PID algorithm exhibits the greatest self‐adaptability, robustness, and steady‐state performance.

Source: Journal of Field Robotics

DOI: 10.1002/rob.70160

Authors: Jun Zhong, Hongshan Feng, Xia Lu, Zhichao Wang

Institution: Hohai University

Language: English

Topic: Advanced Algorithms and Applications

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funder: National Natural Science Foundation of China