This article investigates the issue of adaptive finite‐time tracking control for a category of output‐constrained nonlinear systems in a non‐strict‐feedback form. First, by utilizing the structural characteristics of radial basis function neural networks (RBF NNs), a backstepping design method is extended from strict‐feedback systems to a kind of more general systems, and NNs are employed to approximate unknown nonlinear functions. In addition, the system output is constrained to the specified region by applying the barrier Lyapunov function (BLF) technique. Furthermore, the finite‐time stability of the system is proved by employing the Bhat and Bernstein theorem. As a result, an adaptive finite‐time tracking control scheme for the output‐constrained nonlinear systems with non‐strict‐feedback structure is proposed by applying RBF NNs, BLF, finite‐time stability theory, and adaptive backstepping technique. It is demonstrated the finite‐time stability of the system, the prescribed convergence of the system output and tracking error, the boundedness of adaptive parameters and state variables. Finally, a simulation example is implemented to illustrate the effectiveness of the presented neural control scheme.

中文翻译：

---

具有非严格反馈结构的输出受限非线性系统的自适应有限时间跟踪控制

本文研究了一种非严格反馈形式的输出受限非线性系统的自适应有限时间跟踪控制问题。首先，通过利用径向基函数神经网络（RBF NN）的结构特征，将反推设计方法从严格反馈系统扩展到一种更通用的系统，并使用NN来近似未知的非线性函数。另外，通过使用势垒李雅普诺夫函数（BLF）技术，系统输出被限制在指定的区域。此外，通过使用Bhat和Bernstein定理证明了系统的有限时间稳定性。结果，提出了一种基于RBF神经网络，BLF，非严格反馈结构的输出受限非线性系统的自适应有限时间跟踪控制方案。有限时间稳定性理论和自适应反推技术。它证明了系统的有限时间稳定性，系统输出和跟踪误差的规定收敛性，自适应参数和状态变量的有界性。最后，通过仿真实例说明了所提出的神经控制方案的有效性。