IEEE Trans Neural Netw Learn Syst. 2020 Aug;31(8):2942-2954. doi: 10.1109/TNNLS.2019.2934403. Epub 2019 Sep 4.
In this article, a globally stable adaptive control strategy for uncertain strict-feedback systems is proposed within predefined neural network (NN) approximation sets, despite the presence of unknown system nonlinearities. In contrast to the conventional adaptive NN control results in the literature, a primary benefit of the developed approach is that the barrier Lyapunov function is employed to predefine the compact set for maintaining the validity of NN approximation at each step, thus accomplishing the global boundedness of all the closed-loop signals. Simulation results are performed to clarify the effectiveness of the proposed methodology.
本文针对具有未知系统非线性的不确定严格反馈系统，在预先定义的神经网络（NN）逼近集内提出了一种全局稳定的自适应控制策略。与文献中的传统自适应 NN 控制结果相比，所提出方法的一个主要优点是使用障碍李雅普诺夫函数来预先定义保持 NN 逼近在每个步骤都有效的紧凑集，从而实现所有闭环信号的全局有界性。通过仿真结果验证了所提出方法的有效性。
