In this paper, an adaptive neural network control for stochastic nonlinear systems with uncertain disturbances is proposed. The neural network is considered to approximate an uncertain function in a nonlinear system. And computational burden in operation is reduced by handling the norm of the neural-network vector. However, it will arise chattering issue, which is a challenge to avoid it from the symbolic operation. Further, traditional schemes often view error of estimate as bounded constant, but it is a time-varying function exactly, which may lead control schemes cannot conform to practical situation and guarantee stability of systems. Thus, backstepping technology and the neural network technology combined to stabilize stochastic nonlinear systems together to handle the aforementioned issues. It is proved that the proposed control scheme can guarantee the satisfactory asymptotic convergence performance and predetermined transient tracking error performance. From simulation results, the proposed control scheme is verified that can guarantee the satisfactory effectiveness.

中文翻译：

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一类具有保证瞬态性能的随机非线性不确定系统的自适应神经控制


本文提出了一种针对具有不确定扰动的随机非线性系统的自适应神经网络控制方法。神经网络被认为是逼近非线性系统中的不确定函数。通过处理神经网络向量的范数，减少了运算中的计算负担。然而，它会出现抖振问题，这是从符号操作中避免它的一个挑战。此外，传统方案往往将估计误差视为有界常数，但它实际上是一个时变函数，这可能导致控制方案无法符合实际情况并保证系统的稳定性。因此，反步技术和神经网络技术相结合，共同稳定随机非线性系统来解决上述问题。证明所提出的控制方案能够保证令人满意的渐近收敛性能和预定的瞬态跟踪误差性能。仿真结果验证了所提出的控制方案能够保证令人满意的效果。