モデルベース方策探索による多変数PID制御器の自動チューニング
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
PILCOを拡張し、未知システムの観測データのみから多変数PID制御器を自動調整する枠組みを提案。7自由度ロボットアーム上の倒立振子安定化で有効性を示した。
著者: Andreas Doerr, Duy Nguyen-Tuong, Alonso Marco, Stefan Schaal, Sebastian Trimpe
分類: cs.LG, cs.RO, cs.SY, eess.SY, stat.ML
原文アブストラクト
PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in practice. In this paper, we extend PILCO, a model-based policy search framework, to automatically tune multivariate PID controllers purely based on data observed on an otherwise unknown system. The system's state is extended appropriately to frame the PID policy as a static state feedback policy. This renders PID tuning possible as the solution of a finite horizon optimal control problem without further a priori knowledge. The framework is applied to the task of balancing an inverted pendulum on a seven degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast and data-efficient policy learning, even on complex real world problems.
