论文标题
基于学习的自适应控制具有输入约束的随机线性系统
Learning-Based Adaptive Control for Stochastic Linear Systems with Input Constraints
论文作者
论文摘要
我们提出了一种确定性等效方案，以对标量线性系统的自适应控制，以添加剂，i.i.d。高斯干扰和有限的控制输入约束，而无需先验了解系统参数的界限，也不需要控制方向。假设该系统的偏差稳定，则证明了闭环系统状态的均方根界。最后，提出了数值示例，以说明我们的结果。
We propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i.i.d. Gaussian disturbances and bounded control input constraints, without requiring prior knowledge of the bounds of the system parameters, nor the control direction. Assuming that the system is at-worst marginally stable, mean square boundedness of the closed-loop system states is proven. Lastly, numerical examples are presented to illustrate our results.
