In this study, an adaptive output feedback control with prescribed performance is proposed for unknown pure feedback nonlinear systems with external disturbances and unmeasured states. A novel prescribed performance function is developed and incorporated into an output error transformation to achieve tracking control with prescribed performance. To handle the unknown non‐affine nonlinearities and avoid the algebraic loop problem, the radial basis function neural network (RBFNN) is adopted to approximate the unknown non‐affine nonlinearities with the help of Butterworth low‐pass filter. Based on the output of the RBFNN, the coupled design between sate observer and disturbance observer is presented to estimate the unmeasured states and compounded disturbances. Then, the adaptive output feedback control scheme is proposed for unknown pure feedback nonlinear systems, where a first‐order filter is introduced to tackle with the issue of “explosion of complexity” in the traditional back‐stepping approach. The boundedness and convergence of the closed‐loop system are proved rigorously by utilizing the Lyapunov stability theorem. Finally, simulation studies are worked out to demonstrate the effectiveness of the proposed scheme.

中文翻译：

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基于干扰观测器的纯反馈非线性系统的自适应神经规定性能输出反馈控制

在这项研究中，针对具有外部干扰和不可测状态的未知纯反馈非线性系统，提出了一种具有规定性能的自适应输出反馈控制。开发了新颖的规定性能功能，并将其合并到输出误差转换中，以实现具有规定性能的跟踪控制。为了处理未知的非仿射非线性并避免代数环问题，在Butterworth低通滤波器的帮助下，采用径向基函数神经网络（RBFNN）近似未知的非仿射非线性。基于RBFNN的输出，提出了状态观测器与干扰观测器之间的耦合设计，以估计未测状态和复合干扰。然后，针对未知的纯反馈非线性系统，提出了一种自适应输出反馈控制方案，其中引入了一阶滤波器来解决传统反推方法中的“复杂性爆炸”问题。利用李雅普诺夫稳定性定理，严格证明了闭环系统的有界性和收敛性。最后，通过仿真研究证明了该方案的有效性。