Citation
Truong X. Nghiem, Yash V. Pant, Rahul Mangharam. "Robust Model Predictive Control with Anytime Estimation". 2014 Conference on Decisions and Control (CDC), IEEE, 15, December, 2014.

Abstract
With an increasing autonomy in modern control systems comes an increasing amount of sensor data to be processed, leading to overloaded computation and communication in the systems. For example, a vision-based robot controller processes large image data from cameras at high frequency to observe the robot's state in the surrounding environment, which is used to compute control commands. In real-time control systems where large volume of data is processed for feedback control, the data-dependent state estimation can become a computation and communication bottleneck, resulting in potentially degraded control performance. Anytime algorithms, which offer a trade-off between execution time and accuracy of computation, can be leveraged in such systems. We present a Robust Model Predictive Control approach with an Anytime State Estimation Algorithm, which computes both the optimal control signal for the plant and the (time-varying) deadline/accuracy constraint for the anytime estimator. Our approach improves thé system's performance (concerning both the control performance and the estimation cost) over conventional controllers, which are designed for and operate at a fixed computation time/accuracy setting. We numerically evaluate our approach in an idealized motion model for navigation with both state and control constraints.

Electronic downloads

• https://www.src.org/library/publication/p069675/. (Link to paper on SRC website.)
• http://repository.upenn.edu/mlab_papers/71/

• HTML

  Truong X. Nghiem, Yash V. Pant, Rahul Mangharam. <a
  href="http://www.terraswarm.org/pubs/287.html"
  >Robust Model Predictive Control with Anytime
  Estimation</a>, 2014 Conference on Decisions and
  Control (CDC), IEEE, 15, December, 2014.

• Plain text

  Truong X. Nghiem, Yash V. Pant, Rahul Mangharam.
  "Robust Model Predictive Control with Anytime
  Estimation". 2014 Conference on Decisions and Control
  (CDC), IEEE, 15, December, 2014.

• BibTeX

  @inproceedings{NghiemPantMangharam14_RobustModelPredictiveControlWithAnytimeEstimation,
      author = {Truong X. Nghiem and Yash V. Pant and Rahul
                Mangharam},
      title = {Robust Model Predictive Control with Anytime
                Estimation},
      booktitle = {2014 Conference on Decisions and Control (CDC)},
      organization = {IEEE},
      day = {15},
      month = {December},
      year = {2014},
      abstract = {With an increasing autonomy in modern control
                systems comes an increasing amount of sensor data
                to be processed, leading to overloaded computation
                and communication in the systems. For example, a
                vision-based robot controller processes large
                image data from cameras at high frequency to
                observe the robot's state in the surrounding
                environment, which is used to compute control
                commands. In real-time control systems where large
                volume of data is processed for feedback control,
                the data-dependent state estimation can become a
                computation and communication bottleneck,
                resulting in potentially degraded control
                performance. Anytime algorithms, which offer a
                trade-off between execution time and accuracy of
                computation, can be leveraged in such systems. We
                present a Robust Model Predictive Control approach
                with an Anytime State Estimation Algorithm, which
                computes both the optimal control signal for the
                plant and the (time-varying) deadline/accuracy
                constraint for the anytime estimator. Our approach
                improves thé system's performance (concerning
                both the control performance and the estimation
                cost) over conventional controllers, which are
                designed for and operate at a fixed computation
                time/accuracy setting. We numerically evaluate our
                approach in an idealized motion model for
                navigation with both state and control constraints.},
      URL = {http://terraswarm.org/pubs/287.html}
  }
  

Posted by Barb Hoversten on 21 Mar 2014.