Due to the difficulty encountered in dealing with unstructured system dynamics with unmeasured system state variables, this paper presents a novel observer-based neural network finite-time output control strategy for general high-order nonlinear systems (HNSs). The suggested technique is performed based on the backstepping-like control (BSC) scheme with a hybrid nonlinear disturbance-state observer and norm estimation-based radial basis function neural network (RBFNN). This helps not only reduce the number of estimated parameters but also relax the restriction of using inequality when exploiting the norm estimation concept in a conventional way; thus, retaining the same properties of the original system. Therefore, an observer-based finite-time output feedback control is established to deal with the unstructured dynamical behaviors and satisfying the output tracking regulation with the semi-global practically finite-time stability (SGPFS) guaranteed for the closed-loop system. The effectiveness and workability of the proposed algorithm is verified by a numerical simulation on a specific practical application.

中文翻译：

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高阶不确定非线性系统的新型基于观测器的神经网络有限时间输出控制

由于处理具有不可测量的系统状态变量的非结构化系统动力学遇到困难，本文针对一般高阶非线性系统（HNS）提出了一种新颖的基于观测器的神经网络有限时间输出控制策略。所提出的技术是基于具有混合非线性扰动状态观测器和基于范数估计的径向基函数神经网络（RBFNN）的类反步控制（BSC）方案来执行的。这不仅有助于减少估计参数的数量，而且还可以放宽以传统方式利用范数估计概念时使用不等式的限制；因此，保留了原始系统的相同属性。因此，建立了基于观测器的有限时间输出反馈控制来处理非结构化动态行为并满足输出跟踪调节，并保证闭环系统的半全局实际有限时间稳定性（SGPFS）。通过具体实际应用的数值仿真验证了该算法的有效性和实用性。