Autonomous learning and adaptation of industrial robots using intelligent control algorithms

Abstract

With the continuous advancement of industrial technology and China’s intelligent technology, industrial robots are widely used not only in production and manufacturing industries. This paper presents an autonomous learning robot system, control algorithm, and simulation experiments to study the autonomous learning and adaptation ability of industrial robots. To experiment on the error and tracking trajectory of the industrial robot, the article uses the fuzzy control PID control method based on the traditional learning control system. The results of simulation experiments show that the fuzzy control method in the X, Y, and Z three axes achieved a good control effect, while the speed of the robot arm in the X-axis floats more, while the speed of the robot arm in the Z-axis can reach a maximum of 0.41 m/s. Secondly, when comparing the performance of control algorithms, the fuzzy control PID method has a relatively accurate and stable tracking effect, and the consumption of time is shorter. Finally, the experimental platform for robotic arms is constructed, and the trajectory tracking experiment is carried out according to the fuzzy control PID. Despite uncertainties, fuzzy control PID can still effectively control the robotic arm according to the experimental results. Fuzzy control PID has a significant effect on the autonomous learning and adaptation ability of industrial robots.

Keywords

• intelligent technology
• industrial machine
• fuzzy control pid
• adaptive control
• 68p30