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

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.