Journal
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Volume 48, Issue 5, Pages 1071-1083Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207721.2016.1236423
Keywords
Aerodynamic system; data-driven control; model-free control; position control; reinforcement Q-learning; virtual reference feedback tuning
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Funding
- Romanian National Authority for Scientific Research-the Executive Agency for Higher Education, Research, Development and Innovation Funding, CNCS-UEFISCDI [PN-II-RU-TE-2014-4-0207, PN-II-ID-PCE-2011-3-0109]
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This paper proposes the combination of two model-free controller tuning techniques, namely linear virtual reference feedback tuning (VRFT) and nonlinear state-feedback Q-learning, referred to as a newmixed VRFT-Q learning approach. VRFT is first used to find stabilising feedback controller using input-output experimental data from the process in a model reference tracking setting. Reinforcement Q-learning is next applied in the same setting using input-state experimental data collected under perturbed VRFT to ensure good exploration. The Q-learning controller learned with a batch fitted Q iteration algorithm uses two neural networks, one for the Q-function estimator and one for the controller, respectively. The VRFT-Q learning approach is validated on position control of a two-degrees-of-motion open-loop stable multi input-multi output (MIMO) aerodynamic system (AS). Extensive simulations for the two independent control channels of theMIMO AS show that the Q-learning controllers clearly improve performance over the VRFT controllers.
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