Center for Control and Optimization, School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.
Center for Control and Optimization, School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.
Neural Netw. 2018 Mar;99:123-133. doi: 10.1016/j.neunet.2017.12.013. Epub 2018 Jan 5.
This paper is concerned with the problem of decentralized adaptive backstepping state-feedback control for uncertain high-order large-scale stochastic nonlinear time-delay systems. For the control design of high-order large-scale nonlinear systems, only one adaptive parameter is constructed to overcome the over-parameterization, and neural networks are employed to cope with the difficulties raised by completely unknown system dynamics and stochastic disturbances. And then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions of high-order large-scale systems for the first time. At last, on the basis of Lyapunov stability theory, the decentralized adaptive neural controller was developed, and it decreases the number of learning parameters. The actual controller can be designed so as to ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges in the small neighborhood of zero. The simulation example is used to further show the validity of the design method.
本文研究了不确定高阶大尺度随机非线性时滞系统的分散自适应反步状态反馈控制问题。针对高阶大尺度非线性系统的控制设计，仅构建一个自适应参数来克服过度参数化问题，并采用神经网络来应对完全未知的系统动态和随机干扰带来的困难。然后，首次使用适当的李雅普诺夫-克拉索夫斯基函数和双曲正切函数的性质来处理高阶大尺度系统的未知不匹配时滞相互作用。最后，基于李雅普诺夫稳定性理论，设计了分散自适应神经网络控制器，减少了学习参数的数量。可以设计实际控制器，以确保闭环系统中的所有信号都是半全局一致最终有界（SGUUB），并且跟踪误差在零的小邻域内收敛。仿真示例进一步说明了设计方法的有效性。
