发表机构
University of Maryland（马里兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出信念信息双控制算法，结合滚动时域选择与期望减少约束，在参数不确定下实现混合系统几乎必然目标集收敛，并通过两步前瞻验证递归可行性，显著降低装配误差。
AI 中文摘要
在参数不确定性下将混合系统控制到目标集，可能需要采取信息性动作，这些动作会暂时将系统状态驱离指定集合。我们提出了一种信念信息双控制算法，该算法将信念空间滚动时域选择与目标集进展函数的非负期望减少约束相结合。为适应探索性偏差，一个标量控制器状态限制了约束的累积松弛量。我们算法的两步前瞻选择利用预测观测在评估后续动作的可行性之前更新参数信念。在距离比较界限、正确条件预测和递归可行性的条件下，我们证明了在决策时刻目标集几乎必然收敛，并界定了期望进展函数值之和以及期望邻域进入时间。上置信界将这些收敛保证扩展到具有可求和误差概率的有界模型样本。在具有未知控制方向的平面调节中，我们验证了递归可行性：所提出的两步选择在11个决策内将欧几里得状态范数降至0.01以下，无论符号如何，而短视的一步选择在零输入后立即失去可行性。在模拟的双臂装配任务中，我们算法的一步实现利用间隙反馈在标称摩擦下完成装配，在方法完成时刻，其无穷范数相对位置误差比参考跟踪基线低96.4%。在较低摩擦下，其后验条件化成功检测到空可行集，而固定先验替代方案则允许违反条件减少约束的动作。项目页面：此https URL。
英文摘要
Controlling a hybrid system to a target set under parameter uncertainty can require informative actions that temporarily drive the system state away from the specified set. We propose a belief-informed dual-control algorithm that combines belief-space receding-horizon selection with an expected-decrease constraint on a nonnegative target-set progress function. To accommodate exploratory deviations, a scalar controller state bounds the constraint's cumulative slack. Our algorithm's two-step lookahead selection uses predicted observations to update the parameter belief before evaluating the subsequent action's admissibility. Under distance-comparison bounds, correct conditional prediction, and recursive feasibility, we prove almost-sure target-set convergence at decision times and bound both the sum of expected progress-function values and the expected neighborhood-entry time. Upper confidence bounds extend these convergence guarantees to bounded model samples with summable error probabilities. In planar regulation with an unknown control direction, we verify recursive feasibility: the proposed two-step selection reduces the Euclidean state norm below 0.01 within 11 decisions for either sign, whereas a myopic one-step selection loses admissibility immediately after zero input. In a simulated bimanual assembly task, our algorithm's one-step implementation uses clearance feedback to complete the assembly under nominal friction, yielding a 96.4% lower infinity-norm relative-position error than the reference-tracking baseline at the method's completion time. At lower friction, its posterior conditioning successfully detects an empty admissible set, whereas a fixed-prior alternative admits an action that violates the conditional decrease constraint. Project page: https://clintonenwerem.com/belief-hybrid-control/.
Comments8 pages, 5 figures, 6 tables. Project page: https://clintonenwerem.com/belief-hybrid-control/
