most citedReal-Time Measurement-Driven Reinforcement Learning Control Approach for Uncertain Nonlinear Systems

20 citations · 46 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SY20231 cited

A Policy Iteration Approach for Flock Motion Control

Shuzheng Qu, Mohammed Abouheaf, Wail Gueaieb +1

The flocking motion control is concerned with managing the possible conflicts between local and team objectives of multi-agent systems. The overall control process guides the agent…

eess.SY20237 cited

A Data-Driven Model-Reference Adaptive Control Approach Based on Reinforcement Learning

Mohammed Abouheaf, Wail Gueaieb, Davide Spinello +1

Model-reference adaptive systems refer to a consortium of techniques that guide plants to track desired reference trajectories. Approaches based on theories like Lyapunov, sliding…

eess.SY202316 cited

An Adaptive Fuzzy Reinforcement Learning Cooperative Approach for the Autonomous Control of Flock Systems

Shuzheng Qu, Mohammed Abouheaf, Wail Gueaieb +1

The flock-guidance problem enjoys a challenging structure where multiple optimization objectives are solved simultaneously. This usually necessitates different control approaches t…

eess.SY202320 cited

Real-Time Measurement-Driven Reinforcement Learning Control Approach for Uncertain Nonlinear Systems

Mohammed Abouheaf, Derek Boase, Wail Gueaieb +2

The paper introduces an interactive machine learning mechanism to process the measurements of an uncertain, nonlinear dynamic process and hence advise an actuation strategy in real…

cs.LG20232 cited

Reinforcement Learning-based Wavefront Sensorless Adaptive Optics Approaches for Satellite-to-Ground Laser Communication

Payam Parvizi, Runnan Zou, Colin Bellinger +2

Optical satellite-to-ground communication (OSGC) has the potential to improve access to fast and affordable Internet in remote regions. Atmospheric turbulence, however, distorts th…