The Effect Of The Choice Of Feedforward Controllers On The Accuracy Of Low Gain Controlled Robots

High feedback gains cannot be used on all robots due to sensor noise, time delays or interaction with humans. The problem with low feedback gain controlled robots is that the accuracy of the task execution is potentially low. In this paper we investigate if trajectory optimization of feedback-feedforward controlled robots improves their accuracy. For rest to-rest motions, we find the optimal trajectory indirectly by numerically optimizing the corresponding feedforward controller for accuracy. A new performance measure called the Manipulation Sensitivity Norm (MSN) is introduced that determines the accuracy under most disturbances and modeling errors. We tested this method on a two DOE robotic arm in the horizontal plane. The results show that for all feedback gains we tested, the choice for the trajectory has a significant influence on the accuracy of the arm (viz. position errors being reduced from 2.5 cm to 0.3 cm). Moreover, to study which features of feedforward controllers cause high or low accuracy, four more feedforward controllers were tested. Results from those experiments indicate that a trajectory that is smooth or quickly approaches the goal position will be accurate.