Safety-Critical Adaptive Spiking Multilayer Neural Control Of Nonlinear Systems

Abstract

This paper introduces a novel safety-critical adaptive spiking neural network (SNN) control framework for uncertain nonlinear systems. On the safety side, we formulate the Augmented Barrier States (ABS) methodology into a unified safety-embedded tracking framework, establishing rigorous safety equivalence results that embed safety constraints directly within the closed-loop dynamics. On the learning side, a deep SNN architecture is developed with online adaptation enabled through direct error-driven weight update laws applied at every layer. Unlike conventional gradient approaches that struggle with instability due to the discontinuous nature of spike generation, the proposed method incorporates singular value decomposition (SVD) to regularize gradient flow, enhancing stability and convergence during online learning. The resulting controller guarantees uniformly ultimately bounded tracking while strictly preserving safety constraints for nonlinear systems, ensuring both adaptability and formal safety guarantees. Simulation studies on two-link manipulator validate the framework, demonstrating robust safety preservation, efficient real-time adaptation, and 80% computational energy savings compared to existing ANN-based approaches.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Comments

Army Research Office, Grant W911NF-24-2-0178

Keywords and Phrases

Adaptive Tracking Control; Barrier States; Safety-Critical Control; Spiking Neural Networks

International Standard Book Number (ISBN)

979-8-3315-9381-0; 979-8-3315-9382-7

International Standard Serial Number (ISSN)

0743-1619

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2026

Link to Full Text

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