Case-studies; Classical control; Control approach; Demand sensing; Energy-efficient design; Information transmission; Neuromorphic; Neuromorphic hardwares; On demands; Sensing information; Control and Systems Engineering; Modeling and Simulation; Control and Optimization
Abstract :
[en] Neuromorphic control is receiving growing attention due to the advantages it brings over more classical control approaches, including: sparse and on-demand sensing, information transmission, and actuation; energy-efficient designs and realizations in neuromorphic hardware; event-based signal processing and control signal computation. In this note, we suggest a possible path toward formalizing neuromorphic control systems. We apply the proposed framework to a rhythmic control case study and apply mature control theoretical approaches like describing function analysis, fast-slow analysis, discrete and hybrid systems, and robust optimization.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Medvedeva, Taisia; Cinvestav, Automatic Control Department, Mexico
Castaños, Fernando; Cinvestav, Automatic Control Department, Mexico
Franci, Alessio ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Brain-Inspired Computing
Language :
English
Title :
Formalizing Neuromorphic Control Systems A General Proposal and A Rhythmic Case Study
Publication date :
2025
Event name :
2025 IEEE 64th Conference on Decision and Control (CDC)
Event place :
Rio de Janeiro, Bra
Event date :
09-12-2025 => 12-12-2025
Main work title :
2025 IEEE 64th Conference on Decision and Control, CDC 2025
Publisher :
Institute of Electrical and Electronics Engineers Inc.
MathWorks (United States) MERL - Mitsubishi Electric Research Laboratories (United States) Quanser (Canada) SIAM - Society for Industrial and Applied Mathematics
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