论文标题
非线性输出反馈随机模型预测控制的双重控制效应保留公式和约束
A dual-control effect preserving formulation for nonlinear output-feedback stochastic model predictive control with constraints
论文作者
论文摘要
我们为近似约束的非线性输出反馈随机模型预测控制提出了一种公式。从理想但棘手的随机最佳控制问题（OCP）开始，该问题涉及对输出依赖性策略的优化，我们使用线性化对不确定性进行线性化来得出可易于近似值的可访问，其中包括对输出模型的知识。这使我们能够准确计算OCP的外部功能的期望值。至关重要的是，双重控制效应通过此近似值保留。因此，最终的控制器意识到投入的选择如何影响将来可用的信息，从而影响后续控制。
We propose a formulation for approximate constrained nonlinear output-feedback stochastic model predictive control. Starting from the ideal but intractable stochastic optimal control problem (OCP), which involves the optimization over output-dependent policies, we use linearization with respect to the uncertainty to derive a tractable approximation which includes knowledge of the output model. This allows us to compute the expected value for the outer functions of the OCP exactly. Crucially, the dual control effect is preserved by this approximation. In consequence, the resulting controller is aware of how the choice of inputs affects the information available in the future which in turn influences subsequent controls.
