ガウス過程大域最適化に基づくLQRの自動チューニング
Automatic LQR Tuning Based on Gaussian Process Global Optimization
ベイズ最適化（エントロピーサーチ）を用いてLQR制御器のゲインを実験データから自動調整する枠組みを提案し、7自由度ロボットアームによる倒立振子の安定化で有効性を示した。
著者: Alonso Marco, Philipp Hennig, Jeannette Bohg, Stefan Schaal, Sebastian Trimpe
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Results of a two- and four-dimensional tuning problems highlight the method's potential for automatic controller tuning on robotic platforms.
