arXiv subjects

Elena Petri

Publications and source records attributed to Elena Petri.

2 recordsLinked to original sources

arXiv2026

Robust stabilization with spiking neuronal communication

Neuromorphic engineering develops hardware and software systems inspired by biological neurons, with the goal of achieving energy-efficient, low-latency, robust, and adaptive computation, communication and control. Its potential impact on systems and control is significant, as it may enable novel approaches to control and estimation by leveraging brain-inspired computation and communication principles. In this context, we present a framework for the robust stabilization of a plant subject to disturbances when the communication between noisy sensors and the controller relies on spiking signals generated by neuron-inspired schemes. The communication scheme consists of a spike encoder on the sensors side, which is based on integrate-and-fire neurons that convert the analog plant output measurement into a spiking signal, and a spike decoder on the controller side inspired by synaptic processing to convert the received spiking signal into an analog signal. We provide design conditions on the spike decoder, the spike encoder as well as on the controller under which the closed-loop system exhibits a practical input-to-state stability property, where the adjustable parameters are the amplitudes of the spikes. The results are shown to be applicable to a class of nonlinear systems as well as to any stabilizable and detectable linear time-invariant system. Numerical simulations on a single-link manipulator illustrate the potential of the approach.

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arXiv2025

Emulation-based Neuromorphic Control for the Stabilization of LTI Systems

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.

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