Title

Data-driven virtual reference feedback tuning and reinforcement Qlearning for model-free position control of an aerodynamic system

Document Type

Conference Proceeding

Publisher

Institute of Electrical and Electronics Engineers Inc

School

School of Engineering

Comments

Originally published as: Radac, R. B., Precup, R. E., & Roman, R. C. (2016). Data-driven virtual reference feedback tuning and reinforcement Qlearning for model-free position control of an aerodynamic system. In Proceedings of the 24th Mediterranean Conference on Control and Automation. 5 August 2016, Article number 7535876, Pages 1126-1132. Available here.

Abstract

This paper compares a linear Virtual Reference Feedback Tuning model-free technique applied to feedback controller tuning based on input-output data with two Reinforcement Qlearning model-free nonlinear state feedback controllers that are tuned using input-state experimental data (ED) in terms of two separate learning techniques. The tuning of the state feedback controllers is done in a model reference setting that aims at linearizing the control system (CS) in a wide operating range. The two learning techniques are validated on a position control case study for an open-loop stable aerodynamic system. The performance comparison of our tuning techniques is discussed in terms of their structural complexity, CS performance, and amount of ED needed for learning.

DOI

10.1109/MED.2016.7535876

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