Sensorless control

Our goal is to control robotic arms without any state feedback. This means that the control signal can only be a current or a voltage as function of time. At first sight, this might seem impossible, since there is no sensory feedback to compensate for model inaccuracies and disturbances. Below you can find solutions we found for these problems.

 

Model inaccuracies

The common view on feedforward control is that it needs an accurate model in order to accurately predict a future state of the system. However, we found out that there are model inaccuracies that do not affect the final position of a motion, when using the right feedforward controller. Having an accurate final position is the main requirement in the task we consider: a pick-and-place task. We optimized the feedforward controllers such that the effect of model inaccuracies on the final position was minimized. The results show that the errors in the final position can be reduced to approximately zero for an inaccurate Coulomb, viscous or torque dependent friction. Furthermore, errors in the final position can be reduced, but not to zero, for an inaccurate inertia or motor constant. In conclusion, we show that for certain model inaccuracies, no feedback is required to eliminate the effect of an inaccurate model on the final position of a motion. 

 

Publications

  • Michiel Plooij, Wouter Wolfslag and Martijn Wisse
    Robust feedforward control of robotic arms with friction model uncertainty
    Robotics and Autonomous Systems vol 70, August 2015
    [DOI] [PDF]
  • M.C. Plooij, M. de Vries, W.J. Wolfslag and M. Wisse
    Optimization of feedforward controllers to minimize sensitivity to model inaccuracies
    International Conference on Intelligent Robots and Systems (IROS) 2013
    [DOI] [PDF]

Disturbances

In the case studied above, only model inaccuracies were considered. This does not tell anything about the case when disturbances are present. Therefore, we also developed an approach to perform repetitive tasks with robotic arms, without the need for feedback (i.e. the control is open loop). The cyclic motions of the repetitive tasks are analyzed using an approach similar to limit cycle theory. We optimize open loop control signals that result in open loop stable motions. This approach to manipulator control was implemented on a two DOF arm in the horizontal plane with a spring on the first DOF. The results show that both in simulation and in hardware experiments, it is possible to create open loop stable cycles. However, the two resulting cycles are different due to model inaccuracies.

 

Publications

  • M.C. Plooij, W.J. Wolfslag and M. Wisse
    Open Loop Stable Control in Repetitive Manipulation Tasks
    International Conference on Robotics and Automation (ICRA) 2014
    [DOI] [PDF]

Model inaccuracies and disturbances

The research described above showed that robotic arms are able to perform cyclic tasks with an openloop stable controller. However, model errors make it hard to predict in simulation what cycle the real arm will perform. This makes it difficult to accurately perform pick and place tasks using an open-loop stable controller. Therefore, we developed an approach to make open-loop controllers follow the desired cycles more accurately. First, we check if the desired cycle is robustly open-loop stable, meaning that it is stable even when the model is not accurate. A novel robustness test using linear matrix inequalities is introduced for this purpose. Second, using repetitive control we learn the open loop controller that tracks the desired cycle. Hardware experiments show that using this method, the accuracy of the task execution is improved to a precision of 2.5cm, which suffices for many pick and place tasks.

 

 Publications

  • Wouter Wolfslag, Michiel Plooij, Robert Babuska and Martijn Wisse
    Learning robustly stable open-loop motions for robotic manipulation
    Robotics and Autonomous Systems vol 66, April 2015
    [DOI] [PDF]

Open loop techniques in systems with feedback

Even when feedback is available, 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. We investigated if trajectory optimization of feedbackfeedforward controlled robots improves their accuracy. For rest-to-rest motions, we found 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 DOF 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.

Publications

  • Michiel Plooij, Wouter Wolfslag and Martijn Wisse
    The effect of the choice of feedforward controllers on the accuracy of low gain controlled robots
    International Conference on Intelligent Robots and Systems (IROS) 2015
    [PDF]
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