title: Asymptotic Fuzzy Neural Network Control for Pure-Feedback Stochastic Systems Based on a Semi-Nussbaum Function Technique.
abstract: 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.
authors.forename: Ci
authors.initials: C
authors.lastname: Chen
authors.forename: Zhi
authors.initials: Z
authors.lastname: Liu
authors.forename: Kan
authors.initials: K
authors.lastname: Xie
authors.forename: Yun
authors.initials: Y
authors.lastname: Zhang
authors.forename: C L Philip
authors.initials: CLP
authors.lastname: Chen
mesh_terms: D005246:Feedback; D017143:Fuzzy Logic; D016571:Neural Networks, Computer; D013269:Stochastic Processes