AI 中文总结
研究基于学习的线性MPC在有输入约束和无界随机噪声时的情况，采用确定性等价设计结合切换MPC控制律与在线RLS参数估计，推导非渐近稳定性界，数值实验验证了理论结果。
AI 中文摘要
本文研究了基于学习的模型预测控制（MPC），用于稳定具有硬输入约束和加性无界次高斯干扰的未知离散时间线性系统。采用确定性等价（CE）设计，将切换MPC控制律与在线正则化最小二乘（RLS）参数估计相结合。得到的切换控制律将MPC与饱和拍控制器相结合，确保全局闭环稳定性。基于最小二乘的非渐近误差界，推导了所提出切换控制器下闭环系统的非渐近、高概率稳定性界。数值实验说明了并支持了理论结果。
英文摘要
This paper studies learning-based model predictive control (MPC) for stabilizing unknown discrete-time linear systems with hard input constraints and additive unbounded sub-Gaussian disturbances. We adopt a certainty-equivalence (CE) design that combines a switching MPC control law with online regularized least-squares (RLS) parameter estimation. The resulting switching control law blends the MPC with a saturated deadbeat controller, ensuring global closed-loop stability. Building upon non-asymptotic error bound of least-squares, we derive non-asymptotic, high-probability stability bounds for the closed-loop system under the proposed switching controller. Numerical experiments illustrate and support the theoretical findings.
Comments16 pages, 1 figure
