Adaptive State-Space Control via Neuromorphic Computing
Description
Neuromorphic hardware for autonomous control logic adaptation in robotics/aerospace
Detailed example
governing matrices of the control model
AI / analytics pattern
Agentic AI: AI systems that perform tasks or make decisions autonomously with minimal human intervention.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
This use case addresses the limitations of traditional, fixed-logic control systems by implementing an adaptive state-space model on neuromorphic hardware. By representing a system's states and transitions as a network of spiking neurons and plastic synapses, the governing matrices of the control model are transformed from static constants into dynamic variables that evolve based on real-time sensory input and operational feedback. This enables the system to autonomously learn from its environment, optimize its own control logic, and adapt to unforeseen conditions, creating a foundation for truly intelligent and resilient autonomous systems in fields such as advanced robotics and aerospace.
Controls / human review
ATO: Not reported; PIA: Not published