Comparing Model Predictive Control and input shaping for improved response of low-impedance robots

With an increasing number of robots that can exhibit compliant behavior for safety in operating near humans (either through passive components or active control), additional methods for controlling these robots are needed. In particular, robot arms with low impedance can be safer for working in delicate environments if the effects of dealing with an underdamped robot system can be mitigated to improve performance. This paper focuses on comparing methods that allow a seven degree of freedom Series Elastic Actuator arm to operate with very low impedance while mitigating unwanted oscillation at the end effector. We show that by implementing feedback linearizion in conjunction with input shaping we can reduce residual oscillation for a seven degree of freedom robot arm. We also show that a Cartesian Model Predictive Controller (MPC) is able to significantly reduce residual oscillations while maintaining compliance. Comparing these two methods shows that for our tests for large displacements, MPC has a maximum overshoot of only 0.26% in the worst case where input shaping has at least 5.80% overshoot even in the best case. In addition, despite the fact that MPC is a feedback controller (unlike the open-loop input shaping method), it is still able to maintain compliance at the joints and end effector where we estimated MPC to exhibit a stiffness of 234 N/m as compared to the nominal low impedance controller with a stiffness of 262 N/m. Similar to input shaping (which is a command generation method), MPC is able to generate these commands without any slewing or path planning.