Photogrammetry-based Dynamic Path Tracking of Industrial Robots Using Adaptive Neuro-PID Control Method and Robust Kalman Filter

Abstract

This paper proposes a novel accurate dynamic path tracking (DPT) method for industrial robots based on photogrammetry sensors and an adaptive neuro-PID (ANPID) control method. First, the pose of the robot's end-effector is detected by the photogrammetry sensor (C-Track stereo camera). It passes through a robust Kalman filter to reduce the noise in the signals. Then, the filtered signals are fed to the ANPID, whose gains are tuned online using an adaptive multi-layer perceptron neural network (AMLPNN). The steepest descent optimization method is adopted online. The cost function is the least mean square of the system states errors. Experimental results on FANUC M-20iA robot show the tracking accuracy reaches ±0.08mm and ±0.04deg, which exhibits the superiority of the proposed method over the conventional methods such as PID (tracking error±0.2mm and ±0.1deg) [4].