Stochastic Adaptive Nonlinear Control With Filterless Least Squares

• 成果类型: Article
• 署名作者: Li, Wuquan; Krstic, Miroslav
• 署名单位: Ludong University; University of California System; University of California San Diego
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3027650
• 发表日期: 2021
• 页码: 3893-3905
• 关键词: adaptive control; Stochastic processes; Nonlinear systems; Stochastic systems; Closed loop systems; Adaptation models; Adaptive control; filterless least squares; stochastic nonlinear systems

摘要

For stochastic strict-feedback nonlinear systems with unknown parameters in the drift terms or the diffusion terms, we develop new least-squares identification schemes without regressor filtering. A key new ingredient in the proposed estimator design is a weighted term with design parameters, which is introduced to deal with the nonlinear terms and stochastic noise. With such an estimator, new adaptive controllers are designed to guarantee that the equilibrium at the origin of the closed-loop system is globally stable in probability, and the states are regulated to zero almost surely. Besides, by suitably selecting the estimator parameters, we prove that the proposed least-squares estimators are convergent, as well as strongly consistent in some special cases. Finally, two simulation examples are given to illustrate the least-squares identification and the adaptive control design.