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<p>In this work, we propose a new adaptive iterative learning radial basis function (RBF) neural network control for uncertain nonlinear systems where the global Lipschitz continuity condition is required only for the input function. In addition, a priori knowledge of the control direction is not required. For this purpose, we employ RBF neural networks to estimate the unknown nonlinear functions in such a way that the weights are adjusted using a proposed adaptive law. Furthermore, it is well known that the Nussbaum function technique is an appropriate choice to deal with the unknown control direction. In our paper, this approach is not adopted and the unknown input function is adjusted using a new algorithm. A further advantage of the proposed control is that there is no restriction on nonlinearities. Using Lyapunov theory, the stability analysis of the closed-loop learning system is guaranteed. Finally, simulation results on perturbed nonlinear system are provided to illustrate the effectiveness of the proposed method.</p>

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Adaptive RBF neural network control for nonlinear systems with unknown control direction using iterative learning method

  • 期刊论文

  • 发表日期：

    2026年6月24日

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  • Farah Bouakrif,
  • Tarek Bensidhoum

摘要

In this work, we propose a new adaptive iterative learning radial basis function (RBF) neural network control for uncertain nonlinear systems where the global Lipschitz continuity condition is required only for the input function. In addition, a priori knowledge of the control direction is not required. For this purpose, we employ RBF neural networks to estimate the unknown nonlinear functions in such a way that the weights are adjusted using a proposed adaptive law. Furthermore, it is well known that the Nussbaum function technique is an appropriate choice to deal with the unknown control direction. In our paper, this approach is not adopted and the unknown input function is adjusted using a new algorithm. A further advantage of the proposed control is that there is no restriction on nonlinearities. Using Lyapunov theory, the stability analysis of the closed-loop learning system is guaranteed. Finally, simulation results on perturbed nonlinear system are provided to illustrate the effectiveness of the proposed method.