Nonlinear Disturbance Compensation and Reference Tracking via Reinforcement Learning with Fuzzy Approximators

Author:

Bayiz Y. Efe,Babuska Robert

Publisher

Elsevier BV

Subject

General Medicine

Reference7 articles.

1. Incorporating prior knowledge in fuzzy model identification;Abonyi;International Journal of Systems Science,2000

2. A convergent actorcritic-based FRL algorithm with application to power management of wireless transmitters;Berenji;IEEE Transactions on Fuzzy Systems,2003

3. Brujeni, L., Lee, J.M., and Shah, S. (2010). Dynamic tuning of PI-controllers based on model-free reinforcement learning methods. In Control Automation and Systems (ICCAS), 2010 International Conference on, 453–458.

4. Efficient model learning methods for actor–critic control;Grondman;IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,2012

5. Kiumarsi-Khomartash, B., Lewis, F.L., Naghibi-Sistani, M.B., and Karimpour, A. (2013). Optimal tracking control for linear discrete-time systems using reinforcement learning. In Proceedings 52nd IEEE Annual Conference on Decision and Control (CDC), 3845–3850. Firenze, Italy.

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