论文标题
通过反向最优性的受限控制器和观察者设计
Constrained Controller and Observer Design by Inverse Optimality
论文作者
论文摘要
模型预测控制（MPC）通常是通过反复试验调节的。当存在一个基线线性控制器，该控制器在没有约束的情况下已经对其进行了很好的调整，并且引入了MPC来强制执行它们时，人们希望避免在不活动的情况下更改原始线性反馈定律。我们将此问题提出为匹配的控制器，类似于[1] - [3]，我们将其扩展到更通用的框架。我们证明，可以针对所有稳定线性控制器计算出一个匹配属性的正定阶段成本矩阵。此外，我们通过将线性观察者与移动地平线估计器（MHE）匹配，也可以证明受约束的估计问题也可以类似地解决。最后，我们在某些示例中讨论了所提出技术的实际实施的各个方面。
Model Predictive Control (MPC) is often tuned by trial and error. When a baseline linear controller exists that is already well tuned in the absence of constraints and MPC is introduced to enforce them, one would like to avoid altering the original linear feedback law whenever they are not active. We formulate this problem as a controller matching similar to [1]-[3], which we extend to a more general framework. We prove that a positive-definite stage cost matrix yielding this matching property can be computed for all stabilizing linear controllers. Additionally, we prove that the constrained estimation problem can also be solved similarly, by matching a linear observer with a Moving Horizon Estimator (MHE). Finally, we discuss various aspects of the practical implementation of the proposed technique in some examples.
