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. ; 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. ; 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.
Sep 7, 2021
Aug 26, 2020
|Neural network-based MRAC control of dynamic nonlinear systems||Sep 7, 2021|
Dzieliński, Andrzej Korbicz, Józef - red. Uciński, Dariusz - red.
Haber, Robert Bars, Ruth Lengyel, Orsolya Kowalczuk, Zdzisław - red.
Hammami, Mohamed Ali Jerbi, Hamadi Korbicz, Józef - red. Uciński, Dariusz - red.
Chen, Wei Khan, Abdul Q. Abid, Muhammmad Ding, Steven X. Korbicz, Józef - red. Uciński, Dariusz - red.
Mnasri, Chaouki Gasmi, Moncef Korbicz, Józef - red. Uciński, Dariusz - red.
Janczak, Andrzej Korbicz, Józef - red. Patton, Ronald J. - red.
Dzieliński, Andrzej Beliczyński, Bartłomiej - red.
Pedro, Jimoh Olarewaju Dahunsi, Olurotimi Akintunde Korbicz, Józef - red. Uciński, Dariusz - red.