Improving the accuracy of an industrial robotic arm using iterative learning control with data fusion of motor angles and imu sensors

Sammanfattning: The estimated position of an industrial robot’s end-effector is crucial for its performance. Contemporary methods for doing the estimation is limited in certain aspects, and alternative methods are in high demand. This work builds on a method previously introduced where an Inertial Measurement Unit (IMU) device is combined with the robot’s system via sensor fusion. The IMU must be calibrated before its signal is used in sensor fusion and this work implements and builds on current cutting-edge methods for calibration. Sensor fusion is a crucial part of the method and here a complementary filter is used. The finished estimation is then used with Iterative Learning Control (ILC) to investigate if the accuracy can be further improved and also test its viability. Results from ILC show that the IMU can indeed be used to estimate the end-effector's trajectory but that sensor fusion is mandatory. Further research could be done to allow the estimation to be done online instead of offline and ILC could be tested more extensively.