IEEE Trans Neural Netw Learn Syst. 2021 Jul;32(7):3268-3273. doi: 10.1109/TNNLS.2020.3009871. Epub 2021 Jul 6.
This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural networks are utilized to cope with the unknown nonlinear functions. To present the finite-time adaptive neural control strategy, a new criterion of practical finite-time stability is first developed. Compared with the traditional command filter technique, the main advantage is that the improved error compensation signals are designed to remove the filtered error and the Levant differentiators are introduced to approximate the derivative of the virtual control signal. The finite-time adaptive neural controller is proposed via the new command filter backstepping technique, and the tracking error converges to a small neighborhood of the origin in finite time. Finally, the simulation results are provided to testify the validity of the proposed method.
本简报关注具有任意切换和滞后输入的切换非线性系统的有限时间跟踪控制问题。利用神经网络来处理未知的非线性函数。为了提出有限时间自适应神经网络控制策略，首先开发了一个新的实用有限时间稳定性准则。与传统的命令滤波技术相比，主要优点是设计了改进的误差补偿信号以消除滤波误差，并引入了莱维微分器来逼近虚拟控制信号的导数。通过新的命令滤波反演技术提出了有限时间自适应神经网络控制器，跟踪误差在有限时间内收敛到原点的小邻域。最后，提供了仿真结果以验证所提出方法的有效性。
