School of Electrical and Control Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China.
Center for Control and Optimization, School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
ISA Trans. 2019 Jan;84:55-68. doi: 10.1016/j.isatra.2018.09.019. Epub 2018 Oct 1.
In this paper, the problem of decentralized adaptive neural backstepping control is investigated for high-order stochastic nonlinear systems with unknown interconnected nonlinearity and prescribed performance under arbitrary switchings. For the control of high-order nonlinear interconnected systems, it is assumed that unknown system dynamics and arbitrary switching signals are unknown. First, by utilizing the prescribed performance control (PPC), the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, at each recursive step, only one adaptive parameter is constructed to overcome the over-parameterization, and RBF neural networks are employed to tackle the difficulties caused by completely unknown system dynamics. At last, based on the common Lyapunov stability method, the decentralized adaptive neural control method is proposed, which decreases the number of learning parameters. It is shown that the designed common controller can ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the prescribed tracking control performance is guaranteed under arbitrary switchings. The simulation results are presented to further illustrate the effectiveness of the proposed control scheme.
本文针对具有未知互联非线性和任意切换的高阶随机非线性系统，研究了分散自适应神经网络反步控制问题。对于高阶非线性互联系统的控制，假设未知系统动态和任意切换信号是未知的。首先，通过利用规定性能控制（PPC），可以保证规定的跟踪控制性能，同时消除了对初始误差的要求。其次，在每个递归步骤中，仅构造一个自适应参数，以克服过参数化问题，并采用 RBF 神经网络来解决由完全未知系统动态引起的困难。最后，基于共同 Lyapunov 稳定性方法，提出了分散自适应神经网络控制方法，减少了学习参数的数量。结果表明，所设计的通用控制器可以保证闭环系统中的所有信号都是半全局一致最终有界（SGUUB），并且在任意切换下都可以保证规定的跟踪控制性能。给出了仿真结果，进一步验证了所提出的控制方案的有效性。
