Adaptive prescribed-time control for stochastic nonlinear systems over infinite horizon: A dynamic threshold strategy with application to ship maneuvering systems.

Authors

Yixuan Yuan, Liping Xie, Junsheng Zhao, Kanjian Zhang

Published in

ISA transactions. Sep 12, 2026. Epub Sep 12, 2026.

Abstract

This paper studies the prescribed-time tracking control problem with output constraints for stochastic nonlinear systems over an infinite horizon, motivated by ship maneuvering dynamics. The steady-state tracking accuracy of existing methods is uncertain due to unknown system parameters, which may fail to meet high-precision requirements. To address this issue, an adaptive prescribed-time control framework based on a dynamic-threshold mechanism is developed, which extends the time-accuracy regulation idea to output-constrained stochastic nonlinear systems. A time-varying asymmetric barrier Lyapunov function (BLF) is constructed to enforce output constraints. By incorporating a finite-time command filter and neural networks, the proposed approach alleviates the explosion of complexity in backstepping and approximates unknown nonlinearities. Simulation studies based on a ship maneuvering model demonstrate that the closed-loop system is bounded in probability. Moreover, the system output converges to a prescribed precision neighborhood within the specified time while the output constraints are satisfied at all times.

PMID:
42754494
Bibliographic data and abstract were imported from PubMed on 18 Sep 2026.

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