一类函数完全未知的随机非线性系统自适应H∞跟踪控制
Adaptive H-infinity tracking control for a class of stochastic nonlinear systems with completely unknown functions

针对一类函数完全未知的严格反馈随机非线性系统, 提出了一种基于backstepping技术的鲁棒 H∞自适应神经跟踪控制器设计的新方法. 该方法可在随机非线性系统是依概率一致最终有界的情况下, 保证随机非线性系统 H∞性能指标, 且 H∞踪踪控制器容易获得. 同时该方法去除了一些文献中神经网络逼近误差需要平方可积的假设. 文中使用RBF神经网络逼近打包的未知非线性函数. 所设计的控制器能够保证闭环系统跟踪误差及其它所有信号都是依概率有界的, 且对外界干扰具有鲁棒 H∞抑制作用. 最后, 仿真结果验证了所提方法的有效性和正确性.
In this paper, a novel approach based on backstepping technique is proposed to design an adaptive robust H∞ tracking controller for a class of strict feedback stochastic nonlinear systems, in which all the functions are unknown. This method can guarantee that the controlled system has an H∞ performance index under the condition that the stochastic nonlinear system is uniformly ultimately bounded in probability and the H∞ tracking controller can be obtained easily. At the same time, based on the control scheme, the assumption that approximation errors must be square-integral in some literature has been eliminated. RBF neural networks are used to approximate the packaged unknown nonlinear functions. The designed controller can guarantee that the tracking error and other all the signals in the closed-loop system are bounded in probability, and the controlled system has an H∞ disturbance attenuation performance for external perturbations. Finally, the simulation results are given to demonstrate the feasibility and validity of the proposed method