IEEE Trans Neural Netw Learn Syst - Adaptive NN Control of a Class of Nonlinear Systems With Asymmetric Saturation Actuators.

Tópicos

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Resumo

In this note, adaptive neural network (NN) control is investigated for a class of uncertain nonlinear systems with asymmetric saturation actuators and external disturbances. To handle the effect of nonsmooth asymmetric saturation nonlinearity, a Gaussian error function-based continuous differentiable asymmetric saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided using dynamic surface control. Using radial basis function NN, adaptive control is developed to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design constants. The effectiveness of the proposed control is demonstrated in the simulation study.

Resumo Limpo

note adapt neural network nn control investig class uncertain nonlinear system asymmetr satur actuat extern disturb handl effect nonsmooth asymmetr satur nonlinear gaussian error functionbas continu differenti asymmetr satur model employ backstep techniqu can use control design explos complex tradit backstep design avoid use dynam surfac control use radial basi function nn adapt control develop guarante signal closedloop system semiglob uniform ultim bound track error converg small neighborhood origin appropri choos design constant effect propos control demonstr simul studi

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