Mahmoud, Eslam (University of Paris-Saclay IBISC-EA4526), Mammar, Said (University of Evry, IBISC Lab), SMAILI, Mohand (IBISC)

Model-Free Optimal Static Output Feedback Control Using Integral Reinforcement Learning

Scheduled for presentation during the Regular Session "Adaptive control" (WeAA), Wednesday, June 11, 2025, 11:50−12:10, Auditorium

33rd Mediterranean Conference on Control and Automation, June 10-13, 2025, Tangier, Morocco

This information is tentative and subject to change. Compiled on May 9, 2025

Abstract

This paper presents a novel model-free static output feedback (SOF) control strategy for continuous-time systems, leveraging integral reinforcement learning (IRL) within an off-policy framework. In many practical scenarios, full-state measurements are unavailable, requiring control based solely on sensor outputs. The proposed method addresses this challenge by utilizing only input-output data, avoiding the need for state estimators or observers. The approach offers a practical balance of simplicity, stability, and performance, making it a more accessible alternative than dynamic output feedback methods. The proposed algorithm is adaptive and suits real-world applications involving uncertain or unknown system dynamics. Simulation results demonstrate the method's effectiveness, with comparative analysis showing its better performance over existing model-free approaches.