Robust Model Predictive Control with Anytime Estimation
Truong X. Nghiem, Yash V. Pant, Rahul Mangharam

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.

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  • 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.

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