Work

On-Policy Data-Driven Linear Quadratic Optimal Control of SISO Systems via Model Reference Adaptive Reinforcement Learning

Year: 2025

Type: conference-paper

Abstract: In this paper, we address the problem of on-policy data-driven linear quadratic optimal control for continuous-time single-input single-output systems. Assuming that the plant is minimum phase and has relative degree one, we propose model reference adaptive reinforcement learning – an approach with theoretical guarantees that combines learning and model reference adaptive control. The developed algorithm features an adaptive output-feedback controller that tracks a parameter-varying reference model, whose behavior is shaped by a discrete-time optimizer. For the resulting hybrid closed-loop system, we establish semi-global boundedness of the solutions and show that, under persistency of excitation induced by a dither signal, the applied policy converges to the optimal one.

Source: Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes

DOI: 10.1109/cdc57313.2025.11312870

Authors: Alessandro Bosso, Marco Borghesi, Andrea Serrani, Giuseppe Notarstefano, Andrew R. Teel

Institutions: University of Bologna, University of California, Santa Barbara

Language: English

Topic: Adaptive Dynamic Programming Control

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7123338399

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