Home /Research /Nonlinear Disturbance Compensation and Reference Tracking via Reinforcement Learning with Fuzzy Approximators
LEARNING

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

Yağiz E. Bayiz, Robert Babuška

Year
2014
Citations
10

Keywords

Control theory (sociology)Reinforcement learningController (irrigation)Nonlinear systemFuzzy logicComputer scienceProcess (computing)Payload (computing)Control engineeringCompensation (psychology)

Related papers

Browse all LEARNING papers