Emulation-based Neuromorphic Control for the Stabilization of LTI Systems

Abstract

Neuromorphic engineering aims at designing computing and control systems inspired by the neurons and the brain. For the control community, neuromorphic control is an emerging topic that focuses on designing event-based spiking controllers in the form of spiking neural networks (SNNs). At present, systematic methods for designing and analyzing such controllers are lacking. Therefore in this paper we present a systematic approach for stabilizing linear time-invariant (LTI) systems using SNN-based controllers, in the form of a network of integrate-and-fire neurons, whose input is the measured output from the plant, and which generate spiking control signals. The new approach consists of a two-step emulation-based design procedure. In the first step, we establish conditions on the neuron parameters to ensure that the spiky signal generated by a pair of neurons emulates any continuous-time signal input to the neurons with arbitrary accuracy in terms of a special metric for spiky signals. In the second step, we propose a novel stability notion, called spiky-Input-to-State Stability (sISS) building on this metric, and prove that an asymptotically stable LTI system has this sISS property. By combining these steps, a certifiable practical stability property of the closed-loop system can be established. The approach is illustrated in a numerical case study.