论文标题
最佳LQG控制的行为反馈
Behavioral Feedback for Optimal LQG Control
论文作者
论文摘要
在这项工作中，我们从行为的角度重新审视了线性二次高斯（LQG）最佳控制问题。由行为模型对数据驱动控制的适用性的促进，我们首先重新制定了输入输出行为空间中LQG问题的重新制定，并获得了最佳解决方案的完整表征。特别是，我们表明最佳LQG控制器可以表示为静态行为反馈增益，从而消除了对状态空间方法的动态状态估计特征的需求。最佳LQG增益的静态形式也使其可以通过梯度下降来计算，我们通过数值实验进行了研究。此外，我们在数据驱动的控制设置中，从专家演示中学习最佳LQG控制器。
In this work, we revisit the Linear Quadratic Gaussian (LQG) optimal control problem from a behavioral perspective. Motivated by the suitability of behavioral models for data-driven control, we begin with a reformulation of the LQG problem in the space of input-output behaviors and obtain a complete characterization of the optimal solutions. In particular, we show that the optimal LQG controller can be expressed as a static behavioral-feedback gain, thereby eliminating the need for dynamic state estimation characteristic of state space methods. The static form of the optimal LQG gain also makes it amenable to its computation by gradient descent, which we investigate via numerical experiments. Furthermore, we highlight the advantage of this approach in the data-driven control setting of learning the optimal LQG controller from expert demonstrations.
