Asymptotic Fuzzy Neural Network Control for Pure-Feedback Stochastic Systems Based on a Semi-Nussbaum Function Technique
针对纯反馈随机系统，提出一种渐近模糊神经网络控制方法，通过半Nussbaum函数技术使跟踪误差在概率意义下渐近稳定，突破了传统有界结果。
Most existing control results for pure-feedback stochastic systems are limited to a condition that tracking errors are bounded in probability. Departing from such bounded results, this paper proposes an asymptotic fuzzy neural network control for pure-feedback stochastic systems. The control goal is realized by proposing a novel semi-Nussbaum function-based technique and employing it in adaptive backstepping controller design. The proposed Nussbaum function is integrated with adaptive control technique to guarantee that the tracking error is asymptotically stable in probability.