无状态估计的部分可观测马尔可夫决策过程的线性二次高斯解：一种最小方差方法
LQG solution for POMDP without estimating states: A minimum variance approach
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中文总结 AI 辅助
研究离散时间线性时不变系统在测量不完整和有噪声时的控制，利用最小方差对偶性设计不依赖状态估计的LQG控制器，将控制输入表示为测量值和先前输入的线性函数，经理论证明和数值实验验证了方法的有效性。
中文摘要 AI 辅助
本文研究了在测量不完整和有噪声情况下离散时间线性时不变（LTI）系统的控制问题。具体而言，我们专注于设计一种不依赖显式状态估计的线性二次高斯（LQG）控制器。通过利用最小方差对偶性，我们的方法可将当前控制输入表示为可用测量值和先前应用输入的线性函数，成功将任务简化为一个易于处理的确定性优化问题。我们为该框架提供了理论依据，并通过数值实验证明了其实际有效性。
英文摘要
This paper investigates the control of discrete-time linear time-invariant (LTI) systems subject to incomplete and corrupted measurements. Specifically, we focus on designing a Linear Quadratic Gaussian (LQG) controller without relying on explicit state estimation. By leveraging minimum variance duality, our approach allows the current control input to be represented as a linear function of available measurements and previously applied inputs, successfully reducing the task to a tractable deterministic optimization problem. We provide theoretical justification for this framework and demonstrate its practical effectiveness through numerical experiments.
发表机构
- Department of Artificial Intelligence at Indian Institute of Technology Kharagpur（印度理工学院Khargapur人工智能系）
机构由 AI 辅助整理，请以论文原文为准。
