Periodic Event-Triggered Boundary Control of Neuron Growth with Actuation at Soma
<|reference_start|>Periodic Event-Triggered Boundary Control of Neuron Growth with Actuation at Soma: Exploring novel strategies for the regulation of axon growth, we introduce a periodic event-triggered control (PETC) to enhance the practical implementation of the associated PDE backstepping control law. Neurological injuries may impair neuronal function, but therapies like Chondroitinase ABC (ChABC) have shown promise in improving axon elongation by influencing the extracellular matrix. This matrix, composed of extracellular macromolecules and minerals, regulates tubulin protein concentration, potentially aiding in neuronal recovery. The concentration and spatial distribution of tubulin influence axon elongation dynamics. Recent research explores feedback control strategies for this model, leading to the development of an event-triggering control (CETC) approach. In this approach, the control law updates when the monitored triggering condition is met, reducing actuation resource consumption. Through the meticulous redesign of the triggering mechanism, we introduce a periodic event-triggering control (PETC), updating control inputs at specific intervals, but evaluating the event-trigger only periodically, an ideal tool for standard time-sliced actuators like ChABC. PETC is a step forward to the design of practically feasible feedback laws for the neuron growth process. The PETC strategy establishes an upper bound on event triggers between periodic examinations, ensuring convergence and preventing Zeno behavior. Through Lyapunov analysis, we demonstrate the local exponential convergence of the system with the periodic event-triggering mechanism in the $L^2$-norm sense. Numerical examples are presented to confirm the theoretical findings.<|reference_end|>
@article{demir2024periodic,
    title={Periodic Event-Triggered Boundary Control of Neuron Growth with
  Actuation at Soma},
    author={Cenk Demir, Mamadou Diagne, Miroslav Krstic},
    journal={arXiv preprint arXiv:2404.19206},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.19206},
    primaryClass={math.OC cs.SY eess.SY}
}