ガウス過程を用いたクアッドロータの安全な制御器最適化
Safe Controller Optimization for Quadrotors with Gaussian Processes
SafeOptと呼ばれる安全制約付きベイズ最適化をクアッドロータの制御パラメータ調整に初めて適用し、実機上で安全を保証しながら人手なしで高速に自動チューニングできることを示した。
著者: Felix Berkenkamp, Angela P. Schoellig, Andreas Krause
分類: cs.RO
原文アブストラクト
One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters. Typically, a model of the system is used to obtain an initial controller, but ultimately the controller parameters must be tuned manually on the real system to achieve the best performance. To avoid this manual tuning step, methods from machine learning, such as Bayesian optimization, have been used. However, as these methods evaluate different controller parameters on the real system, safety-critical system failures may happen. In this paper, we overcome this problem by applying, for the first time, a recently developed safe optimization algorithm, SafeOpt, to the problem of automatic controller parameter tuning. Given an initial, low-performance controller, SafeOpt automatically optimizes the parameters of a control law while guaranteeing safety. It models the underlying performance measure as a Gaussian process and only explores new controller parameters whose performance lies above a safe performance threshold with high probability. Experimental results on a quadrotor vehicle indicate that the proposed method enables fast, automatic, and safe optimization of controller parameters without human intervention.
