Adaptive Finite-Time Stabilization of Stochastic Nonlinear Systems Subject to Full-State Constraints and Input Saturation

• 成果类型: Article
• 署名作者: Min, Huifang; Xu, Shengyuan; Zhang, Zhengqiang
• 署名单位: Nanjing University of Science & Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.2990173
• 发表日期: 2021
• 页码: 1306-1313
• 关键词: Nonlinear systems; Stochastic processes; Adaptive systems; uncertainty; stability analysis; Backstepping; Lyapunov methods; Adaptive finite-time control; Barrier Lyapunov function (BLF); full-state constraints; input saturation; semi-globally finite-time stability in probability (SGFSP); stochastic nonlinear systems

摘要

In this article, the adaptive finite-time tracking control is studied for state constrained stochastic nonlinear systems with parametric uncertainties and input saturation. To this end, a definition of semiglobally finite-time stability in probability (SGFSP) is presented and a related stochastic Lyapunov theorem is established and proved. To alleviate the serious uncertainties and state constraints, the adaptive backstepping control and barrier Lyapunov function are combined in a unified framework. Then, by applying a function approximation method and the auxiliary system method to deal with input saturation respectively, two adaptive state-feedback controllers are constructed. Based on the proposed stochastic Lyapunov theorem, each constructed controller can guarantee the closed-loop system achieves SGFSP, the system states remain in the defined compact sets and the output tracks the reference signal very well. Finally, a stochastic single-link robot system is established and used to demonstrate the effectiveness of the proposed schemes.