This paper investigates the tracking control issue of nonlinear stochastic systems subject to time-varying full state constraints and input saturation. By employing both neural network-based approximator and backstepping technique, an adaptive neural network (NN) control approach is presented on the basis of the time-varying barrier Lyapunov function. To surmount the influence of saturation nonlinearity, a Gaussian error function-based continuous differentiable saturation model is introduced such that the actual control in the final backstepping step can be achieved. The designed controller can not only achieve the tracking control objective, but also surmount the impact of input saturation to stochastic system performance. Meanwhile, the norm of NN weight vector is taken as estimated parameter, and it can alleviate computation burden. The presented controller can ensure that all the signals in the closed-loop system are bounded in probability and all state variables are restricted the predefined regions. Finally, simulation results are given to illustrate the effectiveness of the established controller.

本文研究时变全状态约束和输入饱和条件下非线性随机系统的跟踪控制问题。通过同时使用基于神经网络的逼近器和后推技术，提出了基于时变势垒Lyapunov函数的自适应神经网络（NN）控制方法。为了克服饱和度非线性的影响，引入了基于高斯误差函数的连续可微饱和度模型，从而可以实现最终反推步骤中的实际控制。设计的控制器不仅可以达到跟踪控制的目的，而且可以克服输入饱和对随机系统性能的影响。同时，将神经网络权向量的范数作为估计参数，可以减轻计算负担。提出的控制器可以确保闭环系统中的所有信号都以概率为界，并且所有状态变量都被限制在预定义区域中。最后，仿真结果说明了所建立控制器的有效性。