论文标题
基于沉浸式的模型对约束非线性系统的预测控制：多流近似
Immersion-based model predictive control of constrained nonlinear systems: Polyflow approximation
论文作者
论文摘要
在模型预测控制（MPC）的框架中，通常通过反复在线求解优化问题来计算控制输入。对于一般的非线性系统，在线优化问题是非凸的，计算上昂贵甚至棘手。在本文中，我们建议通过计算离散时间非线性系统的高维线性嵌入来避免此问题。该计算依赖于与非线性系统的融合性属性有关的代数条件，并且可以离线实现。利用高维线性模型，我们定义并解决了凸在线MPC问题。我们还提供了Koopman操作员框架下的方法的解释。
In the framework of Model Predictive Control (MPC), the control input is typically computed by solving optimization problems repeatedly online. For general nonlinear systems, the online optimization problems are non-convex and computationally expensive or even intractable. In this paper, we propose to circumvent this issue by computing a high-dimensional linear embedding of discrete-time nonlinear systems. The computation relies on an algebraic condition related to the immersibility property of nonlinear systems and can be implemented offline. With the high-dimensional linear model, we then define and solve a convex online MPC problem. We also provide an interpretation of our approach under the Koopman operator framework.
