Sui Shuai, Chen C L Philip, Tong Shaocheng
IEEE Trans Neural Netw Learn Syst. 2021 Jul;32(7):3196-3205. doi: 10.1109/TNNLS.2020.3010333. Epub 2021 Jul 6.
This article investigates the problem of neural network (NN)-based adaptive backstepping control design for stochastic nonlinear systems with unmodeled dynamics in finite-time prescribed performance. NNs are used to study the uncertain control plants, and the problem of unmodeled dynamics is tackled by the combination of the changing supply function and the dynamical signal function methods. The outstanding contribution of this article is that based on the finite-time performance function (FTPF), a modified finite-time adaptive NN control design strategy is proposed, which makes the controller design simpler. Eventually, by using the Itô's differential lemma, the backstepping recursive design technique, and the FTPFs, a novel adaptive prescribed performance tracking control scheme is presented, which can guarantee that all the variables in the control system are bounded in probability, and the tracking error can converge to a specified performance range in the finite time. Finally, both numerical simulation and applied simulation examples are provided to verify the effectiveness and applicability of the proposed method.
本文研究了具有未建模动态的随机非线性系统在有限时间规定性能下基于神经网络（NN）的自适应反步控制设计问题。神经网络用于研究不确定的控制对象，未建模动态问题通过变化供应函数和动态信号函数方法相结合来解决。本文的突出贡献在于基于有限时间性能函数（FTPF），提出了一种改进的有限时间自适应神经网络控制设计策略，使控制器设计更简单。最终，通过使用伊藤微分引理、反步递归设计技术和FTPF，提出了一种新颖的自适应规定性能跟踪控制方案，该方案可以保证控制系统中的所有变量在概率上有界，并且跟踪误差能够在有限时间内收敛到指定的性能范围内。最后，给出了数值仿真和应用仿真示例，以验证所提方法的有效性和适用性。
