Optimization of time-varying load dynamic response of industrial robot PLC system based on improved model reference adaptive control

Introduction In the process of industrial automation, industrial robots are widely used in complex operations such as welding, assembly, and handling. The dynamic response performance under time-varying load conditions directly affects production efficiency and control quality. Methods To improve the dynamic response speed and control accuracy of industrial robot Programmable Logic Control (PLC) systems under time-varying loads, an improved Model Reference Adaptive Control (MRAC) strategy that combines Fuzzy Correction Adaptive Law (FCAL) and Particle Swarm Optimization (PSO) algorithm is designed. It combines an Extended Kalman Filter (EKF) load observer with a composite control law to adapt to the discrete characteristics of PLC and optimize multi-task scheduling. Results Experiments show that in three scenarios: automotive welding, electronic assembly, and metal cutting, the production efficiency of this system is increased by 20.7%–23.8% compared with traditional PLC methods, and the dynamic response time is shortened from 0.8 s to 0.3 s. The product qualification rate increases from 1.9% to 3.9%, and the positioning error of the assembly robot drops from ±0.1 m to ±0.05 m. The torque fluctuation of the cutting robot motor drops from 1.0 N m to 0.58 N m, the load observation error does not exceed 0.05 N m, and the angular velocity overshoot is less than 1.2%. Discussion Through the deep integration of adaptive control strategy and PLC system, the dynamic response speed and control accuracy of industrial robots under time‐varying load conditions are effectively improved, and production efficiency, product qualification rate, and energy consumption indicators are improved. This study provides reliable technical support for the field of flexible manufacturing.