English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this work, we introduce an adaptive neural network controller for a class   of nonlinear systems. The approach uses two Radial Basis Functions, RBF   networks. The first RBF network is used to approximate the ideal control law   which cannot be implemented since the dynamics of the system are unknown. The   second RBF network is used for on-line estimating the control gain which is a   nonlinear and unknown function of the states. The updating laws for the   combined estimator and controller are derived through Lyapunov analysis.   Asymptotic stability is established with the tracking errors converging to a   neighborhood of the origin. Finally, the proposed method is applied to   control and stabilize the inverted pendulum system

Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator