ρ-POMDP計画によるモデル同定適応制御
Model Identification Adaptive Control with $\rho$-POMDP Planning
システムパラメータを隠れ状態として扱い、信念空間計画（ρ-POMDP）として情報入力設計とモデル同定適応制御を統合し、BiLQRで解く手法を提案。
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著者: Michelle Ho, Arec Jamgochian, Mykel J. Kochenderfer
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Accurate system modeling is crucial for safe, effective control, as misidentification can lead to accumulated errors, especially under partial observability. We address this problem by formulating informative input design and model identification adaptive control (MIAC) as belief space planning problems, modeled as partially observable Markov decision processes with belief-dependent rewards ($\rho$-POMDPs). We treat system parameters as hidden state variables that must be localized while simultaneously controlling the system. We solve this problem with an adapted belief-space iterative Linear Quadratic Regulator (BiLQR). We demonstrate it on fully and partially observable tasks for cart-pole and steady aircraft flight domains. Our method outperforms baselines such as regression, filtering, and local optimal control methods, even under instantaneous disturbances to system parameters.
