Robust neuronal adaptive control for a class of uncertain nonlinear complex dynamical multivariable systems

A B ST R A C T In this paper, we proposed the development of neural adaptive controls to ensure the robustness of uncertain nonlinear multivariable systems. We used two techniques: Robust neural adaptive control and neural indirect adaptive control. The study of the stability and robustness of both techniques was performed by Lyapunov theory. To validate these techniques and discover their effectiveness, a simulation example was considered. The simulation results obtained by these two control techniques have shown the effects of disturbance compensation, good performance tracking data paths and stability control systems. Comparative studies between these two techniques show that the neural indirect adaptive control cannot mitigate the effect of disturbances compared to the robust neural adaptive control.

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