@misc{Debbache_Ghania_Neural,
 author={Debbache, Ghania and Bennia, Abdelhak and Goléa, Noureddine},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 abstract={This paper presents direct model reference adaptive control for a class of nonlinear systems with unknown nonlinearities. The model following conditions are assured by using adaptive neural networks as the nonlinear state feedback controller. Both full state information and observer-based schemes are investigated.},
 abstract={All the signals in the closed loop are guaranteed to be bounded and the system state is proven to converge to a small neighborhood of the reference model state. It is also shown that stability conditions can be formulated as linear matrix inequalities (LMI) that can be solved using efficient software algorithms.},
 abstract={The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulation results are presented to show the effectiveness of the approach.},
 type={artykuł},
 title={Neural network-based MRAC control of dynamic nonlinear systems},
 keywords={neural networks, reference model, nonlinear systems, adaptive control, observers, stability, LMI},
}