Daye Pierre, Ieng Sio-Hoi, Benosman Ryad
StreetLab - Institut de la Vision, Paris, France.
INSERM UMRI S 968, Institut de la Vision, Paris, France.
Front Neurosci. 2019 Aug 21;13:827. doi: 10.3389/fnins.2019.00827. eCollection 2019.
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.
大多数动态系统由离散时间控制器控制。数字控制律设计过程中面临的主要挑战之一是选择合适的采样时间。较小的采样时间会以大量计算为代价提高受控输出的精度。相反，较大的采样时间会以较小的稳定区域为代价降低更新控制律所需要的计算能力。此外，一旦达到设定值，受控输入仍会更新，这使得整个受控系统在能量利用上效率不高。为了提高效率，可以基于受控信号的显著固定变化来更新控制律（增量发送或基于事件的控制器）。与基于时间的离散化一样，必须谨慎选择显著变化的幅度，以避免在设定值附近振荡（例如，如果设定值在两个样本之间），或者避免在以给定精度达到设定值所需的样本数量上不必要地增加。本文提出了一种基于事件间持续时间的新型非线性基于事件的离散化方法。我们证明，我们的新方法可以达到任意精度，而与设定值幅度无关，且不会增加网络数据传输带宽。该方法减少了估计动态系统状态所需的样本总数以及执行器的更新速率，使其在能量利用上更高效。
