Belief-Informed Hybrid Control with Almost-Sure Target-Set Convergence

Key points

• 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.1
• To accommodate exploratory deviations, a scalar controller state bounds the constraint's cumulative slack.1
• 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.1
• 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.1

Sources (1)

• [1]Belief-Informed Hybrid Control with Almost-Sure Target-Set Convergence
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 03:08 AM

  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.

  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.

Extractive summary: sentences quoted from the sources.