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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurosci. | doi: 10.3389/fnins.2019.00827

A theory for sparse event-based closed loop control

 Sio Hoi Ieng1*,  Pierre Daye2* and Ryad Benosman1
  • 1UMR_S968 Inserm/UPMC/CNRS 7210, Université Pierre et Marie Curie, France
  • 2INSERM U968 Institut de la Vision, France

Most dynamic systems are controlled by discrete time controllers. One of the main challenges faced during the design of a digital control law is the selection of the appropriate sampling time. A small sampling time will increase the accuracy of the controlled output at the expense of heavy computations. In contrast, a large sampling time will decrease the computational power needed to update the control law at the expense of a smaller stability region. In addition, once the setpoint is reached, the controlled input is still updated, making the overall controlled system not energetically efficient. To be more efficient, one can update the control law based on a significant fixed change of the controlled signal (send-on-delta or event-based controller). Like for time-based discretization, the amplitude of the significant change must be chosen carefully to avoid oscillations around the setpoint (e.g., if the setpoint is in between two samples) or an unnecessary increase of the samples number needed to reach the setpoint with a given accuracy. This paper proposes a novel non-linear event-based discretization method based on inter-events duration. We demonstrate that our new method reaches an arbitrary accuracy independently of the setpoint amplitude without increasing the network data transmission bandwidth. The method decreases the overall number of samples needed to estimate the states of a dynamical system and the update rate of an actuator, making it more energetically efficient.

Keywords: Dynamic system, feedback control, Control theory, Event-based signal processing, Level crossing sampling

Received: 15 Feb 2019; Accepted: 24 Jul 2019.

Edited by:

Gert Cauwenberghs, Department of Bioengineering, Jacobs School of Engineering, University of California, San Diego, United States

Reviewed by:

Yulia Sandamirskaya, Institute of Neuroinformatics, ETH Zurich, Switzerland
Nabil Imam, Cornell University, United States  

Copyright: © 2019 Ieng, Daye and Benosman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Dr. Sio Hoi Ieng, Université Pierre et Marie Curie, UMR_S968 Inserm/UPMC/CNRS 7210, Paris, 75012, France, sio-hoi.ieng@upmc.fr
Dr. Pierre Daye, INSERM U968 Institut de la Vision, Paris, 75012, Île-de-France, France, pierre.daye@gmail.com