access icon free Adaptive neural data-based compensation control of non-linear systems with dynamic uncertainties and input saturation

In this study, an adaptive neural backstepping control scheme is proposed for a class of strict-feedback non-linear systems with unmodelled dynamics, dynamic disturbances and input saturation. To solve the difficulties from the unmodelled dynamics and input saturation, a dynamic signal and smooth function in non-affine structure subject to the control input signal are introduced, respectively. Radial basis function (RBF) neural networks are used to approximate the packaged unknown non-linearities, and an adaptive neural control approach is developed via backstepping, which guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in mean square. The main contributions of this note lie in that a control strategy is provided for a class of strict-feedback non-linear systems with unmodelled dynamics uncertainties and input saturation, and the proposed control scheme does not require any information of the bound of input saturation non-linearity. Simulation results are used to show the effectiveness of the proposed control scheme.

Inspec keywords: radial basis function networks; closed loop systems; feedback; nonlinear control systems; adaptive control; neurocontrollers; uncertain systems

Other keywords: adaptive neural data-based compensation control; input saturation nonlinearity; strict-feedback nonlinear systems; dynamic disturbances; dynamic signal; smooth function; unmodelled dynamics; closed-loop system; RBF neural networks; radical basis function neural networks; dynamic uncertainties; nonaffine structure; adaptive neural backstepping control

Subjects: Nonlinear control systems; Neurocontrol; Self-adjusting control systems

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http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2014.0709
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