School of Electrical Engineering, Anhui Technical College of Mechanical and Electrical Engineering, Wuhu, China.
Zhejiang Dongfang Polytechnic, Wenzhou, China.
PLoS One. 2024 Oct 24;19(10):e0312345. doi: 10.1371/journal.pone.0312345. eCollection 2024.
In this work, the tracking control problem for a class of nonlinear strict-feedback systems with input nonlinearity is addressed. In response to the influence of input nonlinearity, an auxiliary control system is constructed to compensate for it. To process unknown nonlinear dynamics, radial basis function neural networks (RBFNNs) are introduced to approximate them, and some adaptive updating control laws are designed to estimate unknown parameters. Furthermore, during the dynamic surface control (DSC) design process, first-order low-pass filters are introduced to solve the complexity explosion problems caused by repeated differentiation. After that, an NN-based adaptive dynamic surface tracking controller is proposed to achieve the tracking control. By applying the proposed controller, it can be guaranteed that not only the output of the system can track the desired trajectory, but also that the tracking error can converge to a small neighborhood of zero, while all signals of the closed-loop system are bounded. Finally, the effectiveness of the proposed controller is verified through two examples.
在这项工作中，研究了一类具有输入非线性的非线性严格反馈系统的跟踪控制问题。针对输入非线性的影响，构建了一个辅助控制系统来对其进行补偿。为了处理未知的非线性动力学，引入了径向基函数神经网络（RBFNN）来对其进行逼近，并设计了一些自适应更新控制律来估计未知参数。此外，在动态面控制（DSC）设计过程中，引入了一阶低通滤波器来解决由于重复微分而引起的复杂性爆炸问题。之后，提出了一种基于神经网络的自适应动态面跟踪控制器来实现跟踪控制。通过应用所提出的控制器，可以保证系统的输出不仅能够跟踪期望轨迹，而且跟踪误差能够收敛到零的小邻域内，同时闭环系统的所有信号都是有界的。最后，通过两个例子验证了所提出控制器的有效性。
