论文标题
通过系统水平合成，具有多功能模型不确定性的强大模型预测性控制
Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis
论文作者
论文摘要
我们提出了一种可靠的模型预测控制（MPC）方法，用于具有多功能模型不确定性和加性干扰的离散时间线性系统。在仅存在加性干扰时，已成功使用对线性时变（LTV）状态反馈控制器进行优化。但是，面对模型不确定性，设计LTV状态反馈控制器的影响很难束缚，这是一项挑战。为了解决这个问题，我们提出了一种新颖的方法，以过度陈列于模型不确定性和添加剂扰动的效果，并通过过滤的加性干扰信号。使用系统级综合框架，我们共同搜索强大的LTV状态反馈控制器以及在线不确定性影响的界限，这使我们能够减少保守主义，并最大程度地减少强大MPC中最差的案例成本的上限。我们对文献中的方法和代表性强大的MPC方法进行了全面的数值比较。数值示例表明，我们提出的方法可以在广泛的不确定性参数上显着降低保守主义，而相当的计算工作与基线方法相当。
We propose a robust model predictive control (MPC) method for discrete-time linear systems with polytopic model uncertainty and additive disturbances. Optimizing over linear time-varying (LTV) state feedback controllers has been successfully used for robust MPC when only additive disturbances are present. However, it is challenging to design LTV state feedback controllers in the face of model uncertainty whose effects are difficult to bound. To address this issue, we propose a novel approach to over-approximate the effects of both model uncertainty and additive disturbances by a filtered additive disturbance signal. Using the System Level Synthesis framework, we jointly search for robust LTV state feedback controllers and the bounds on the effects of uncertainty online, which allows us to reduce the conservatism and minimize an upper bound on the worst-case cost in robust MPC. We provide a comprehensive numerical comparison of our method and representative robust MPC methods from the literature. Numerical examples demonstrate that our proposed method can significantly reduce the conservatism over a wide range of uncertainty parameters with comparable computational effort as the baseline methods.
