Work

Adaptive Tuning of Event-Triggered Control based on Machine Learning: Application to Mobile Robotics

Year: 2025

Type: article

Abstract: This paper aims to optimize the frugal navigation of mobile robots by combining event-triggered control with artificial intelligence techniques. Event-triggered control updates the control signal only during significant changes in the system dynamics or its environment, thereby reducing the usage of limited onboard resources. This work is based on a discrete-time linear quadratic event-triggered control approach, which comes with a constant tuning parameter to set the tradeoff between resource utilization, performance, and control effort. Machine learning methods (linear regression and neural network) are explored to make this parameter dynamically adaptive to enable better and sparser trajectory tracking. Both simulation results on a digital twin and experimental results on a real two-wheeled robot show that combining event-triggered control with machine-learning techniques can efficiently improve robot navigation, offering adaptive and robust solutions across various configurations.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.10.220

Authors: Maya Pivert, Inas Belrhazi, Florian Pouthier, Sylvain Durand

Institutions: Centre National de la Recherche Scientifique, Institut National des Sciences Appliquées de Strasbourg, Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie

Language: English

Topic: Stability and Control of Uncertain Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funders: Indian National Science Academy, Agence Nationale de la Recherche

Awards: G2776477858, Navigation in GPS-denied dark environment using aperiodic flash-based photolocation