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.
Full Text
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call